CA2177839A1 - Sudden cardiac death prediction - Google Patents

Abstract

A method and apparatus for predicting susceptibility to sudden cardiac death (208) involves simultaneously assessing cardiac electrical stability (206), represented by either the beat-to-beat alternation in the T-wave of the patient's ECG or dispersion of the QT interval and the autonomic influence on the heart (204), represented by either the magnitude of heart rate variability in the ECG or by baroreceptor sensitivity.

Description

Wo 95/15116 2 ~ 7 7 ~ 3 9 PCTIUS94/13736 m SUDDEN CARDIAC DEATH PREDICTION
STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER
FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT
Patt of the work performed during ~ ' r ' of this imvention utilized U.S. Government funds. The U.S. Government has certain rights in this mvention.
BACKGROUND OF THE INVENTION
1. RELATED APPLICATION
This application is a; in-part of application serial number 07/948,529, filed September 22, 1992, now U.S. Pat. No. 5,265,617; which is a ~ ;. "--m-part of application serial number 07/768,054, filed September 30, 1991, now U.S. Pat. No. 5,148,812; which is a c, in-part of application serial number 07/659,711, filed February 20, 1991, now abandoned.

2. FIELD OF T~E INVENTION
The invention relates to cardiology. More specifically, the invention relates to non-imvasive i~ , and ,, of individuals at risk for sudden cardiac death. Cardiac vulu~,ldlJiliLy to ventricular fibrillation, the mode of sudden death, is dynamically tracked by analysis of an LIUL~I~I;O~

3. RELATED ART
Sudden cardiac death (SCD), which claims over 350,000 lives annually in the United States, results from abrupt disruption of heart rhythm primarily due ~o ventricular fibrillation. Fibrillation occurs when transient neural triggers impmge upon an electrically unstable heart causing normally 2~ 77~

organized electrical activity to become ~ and chaotic. Complete cardiac dy~fi~ iull results.
The first step in preventing sudden cardiac death is identifying those individuals whose hearts are electrically unstable. This is a major objective in cardiology. If vulnerable individuals can be reliably identified non-invasively, then preventiûn will be aided, mass screening will become possible, and l)llA ~ gi~ l II of vulnerable individuals can be tailored to prevent ventricular fibrillation.
~ O ' cardiac electrical stimulation has been used in patients to provide ~luall~ila~iv~ r.-, . -~ ;- " on cl~crf rtihility and on the ~rf~,~,Li~ ,,,,. of their 1' ' O therapy. ullru- 'y, this method requires cardiac ,AIh.,..;,~li.." and introduces the hazard of inadvertent induction of ventricular fibrillation. Therefore, it is used only in severely ill patients and is performed only in hospitals. It is unsuitable for mass screening.
A technique which has shown great promise is that of analyzing alternans in the T-wave of an ~l~llu~aldiuOIalll (ECG). As used throughout this disclosure, the term "T-wave" is defined to mean the portion of an ECG
which includes both the T-wave and the ST segment. Alternans in the T-wave results from different rates of ~ of the muscle cells of the ventricles. The extent to which these cells recover (or repolarize) non-uniformly is the basis for electrical instability of the heart.
The consistent occurrence of alternans in the T-wave prior to fibrillation is well ecf-lhlj~ Thus, detection of alternans promises to be a useful tool in predicting vulnerability to fibrillation, if an accurate method of quantifying the alternans can be developed. The following are examples of cull~ ,iullal attempts to quantify alternation in an ECG signal: Dan R. Adam et al., ~rlu~.ur~iull~ in T-Wave Morphology and S~Cr~rtihility to Ventricular Fibrillation," Journal of Ele~lru~ ;y, vol 17 (3), 209-218 (1984);
Joseph M. Smith et al. "Electrical alternans and cardiac electrical instability,"
Circ~lation, vol. 77, No. 1, 110-121 (1988); U.S. Pat. No. 4,732,157 to Kaplan et al.; and U.S. Pat. No. 4,802,491 to Cohen et al.

wo95llsll6 2 1 7 ~ 8 ~ 9 PCTIUS94113736 Smith et aL and Cohen et al, disclose methods for assessing myocardial electrical instability by power spectrum analysis of the T-wave. These methods derive an alternating ECG I.c,l~I-ol~,~y index from a series of heartbeats. Sample point matrices are constructed and the alterrlating energy at each of the sample points is computed using the analytical method of multi-,1;"" ..c . -~ power spectral estimation which is calculated by ,U~DLlu~Lil.o the discrete Fourier transform of the Hanning-windowed sample auto-correlation function. Tbe alternating ene}gy over the entire set of sample points is summed to generate the total alterrlating energy and then normalized with lû respect to the average waveform to produce an ~alternating ECG Illul~llology index (AEMI)."
While a powerful tool, Fourier power spectrum analysis averages time functions over the entire time series so that rapid .~IIllyLlllll~ . changes, such as those due to neural discbarge and I~ ,l r ' , are not detected because data from these events are intrinsically non-stationary.
Kaplan et al. disclose a method for quantifying cycle-to-cycle variation of a ~llya;~JlOgiC waveform such as the ~CG for the purpose of assessing myocardial electrical stability. A pllyalOlogi~ waveform is digitized and sampled and a scatter plot of the samples is created. Non-linear 1,,., r~" ;"" of the sample points determines a single parameter which attempts to quantify the degree of alternation in the sampled waveform and which is associated with the c~cr~rtihility of the ~ a;ulOgic waveform to enter into an aperiodic or chaotic state. Kaplan et al. suggest that "1.,~ of [this parameter] may provide an index of ECG waveform variability which may provide an improved correlation with cll~rl~rtihility to ventricular fibrillation thanpreviously available indices. " See col.3, lines 15-lg. Whetherventricular fibrillation is a chaotic state, however, is still very much in debate.
See D.T. Kaplan and ~. J. Cohen, "Searching for chaos in fibrillation, " Ann.
I~.Y. Acad. Sci., vol. 591, pp. 367-374, 1990.
Adam et al. disclose a non-invasive method which involves spectral analysis of the alternation from beat-to-beat , ' ~' Oy of the ECG complex.

WO 95/15116 2 ~ 7 ~ 8 ~ q PCT/US94/13736 .

The alternation of T-wave energy from beat-to-beat was measured to generate a T-waYe alternation index ('I'WAI). This technique is unable to detect alternation in waveform luul~ olu~;y which results in alternating wave shapes of equal energy. In addition, the amount of alternation detected per this method is dependent on the static portion of the wave shape. That is, the same amount of alternation r~ 1 on a different amplitude signal will result in different values for the T-wave alternation inde~ such that this technique could completely obscure the presence of alternation in the original waveform ,....,l.l,..l.~;f.c In the absence of an effective method for dynamically 4u.~ iryill~ the magnitude of alternation, i~ r; ;--l of alternans as a precursor of life-threatening allhy ' and provision of a test for cardiac VUIll.,l~;liLy have been 1 ~ lr In addition, the Wll~lliiUII~I attempts to quantify alternans have employed inferior methods of alternans (i.e., ECG) sensing. The ECG
signals used for the Cohen et al. analysis were sensed via epicardial (i.e., heart surface) electrodes or via lateral limb, rostral-caudal, and 11nrr^~
leads. Smith et al. sensed via leads 1, aVF, and Vl 2. Adam et al. utilized ECG lead I "because in this lead the ratio of the amplitude of the pacing stimulus artifact to the amplitude of the QRS complex was usually smallest."
See Adam e~ al. at 210. Lead I, however, provides only limited ;.,ru, I.~ ;u~
regarding the f,lf~,ilu~hyalvlO~i~, processes occurring in the heart.
There have been occasional reports in the human literature noting the presence of T-wave alternans in the precordial leads. However, there has been no suggestion of a superior lead .,..,ri",..,.,;..,. from the body surface which permits ~ lrll~ ~ of alternans as a uu~uliiLa~iv~ predictor of sllc~rtihility to ventricular fibrillation and sudden death. For example, alternans have been observed in precordial leads V~ and V5 during a PCTA
(r~ ,ui u.~,vua Tl, ~ --l Coronary Angioplasty) procedure on a fifty year-old man. M. Joyal et al., "ST-segment alternans during p.,l~.UL~ll..,VUa i ' ' coronary angioplasty," Am. J. Cardiol., vol. 54, pp. 915-916 (1984). Similarly, alternans were noted in precordial leads V~ through V6 on wo 95/15116 PCrNS94113736 2 i 77~39 a forty-four year-old man during and following a treadmill exercise. N. Belic, et al., "ECG . - - ,; f ~ of myocardial ischemia, " Arch. Intern. Meevl., vol.
140, pp. 1162-1165 (1980).
Dispersion of ~ has also been integrally linked to cardiac ~ vlG~ y and has recently received ~-"~ attention as a potential marker for vulnerability to ventricular fibrillation. The basis for this linkageis that the extent of llvvvluc_llv;Ly of recovery of action potentials is directly related to the propensity of the heart to experience multiple re-entrant currents, which initiate and maintain fibrillation and culminate in cardiac arrest. B.
Surawicz, "Ventricular fibrillation," vr. Am. cOn. Cardiol., vol. 5, pp. 43B-54B (1985); and C. Kuo, et al., "(~1..,., .~ ;~1;. ~ and possible mechanism of ventricular arrhythmia dependent on the dispersion of action potential duration," Circ~lanon, vol. 67, pp. 1356-1367 (1983).
The most commonly employed non-invasive approach for measuring dispersion is to obtain body surface maps to define the ~ . il,vti.. ,. of T-wave .Jt~ and thus estimate the degree of unevenness of IcyulGli~GliOll and y to ventricular fibrillation. F. Abildskov, et al., ~The expression of normal ventricular Ir~ in the body surface ~ljctrihlltirn of T
potentials," Clrculation, vol. 54, pp. 901-906 (1976); J. Abildskov and L.
Green, "The recognition of arrhythmia vulnerability by body surface ~ ,vLIu~,Gld;u~lGyll;c mapping," Circv~lanon, vol.75 (suppl. 111), pp.79-83 (1987); and M. Gardner, et al., "Vulnerability to ventricular G llly ' assessment by mapping of body surface potential," C~rcv~la~ion, vol. 73, pp.
684-692 (1986). Although this approach has been in existence for over 15 years, it has received minimal usage in the clinical setting. The basis for thisis that the technique is, .,."1.. . ~u , as it requires over 100 leads on the chest and extensive ~ ;- d analysis. Thus, it is used in only a few specialized research centers.
Recently, these has been interest in analyzing QT interval dispersion in the standard 12-lead ECG as a measure of vulnerability to life-threatening allllyLlllll;Gs. The ' I l,."-~ ".lirl~ required is relatively WO 95115116 ~ ~ 7 l~ dr 3 9 PCI[/US94/13736 'V~ r W~lld as it involves mainly subtraction of a minimum QT interval from a maximum QT inoerval and 1~ the variance of the difference.
For example, it has been found that QT dispersion is an indicator of risk for arrhythmia in patients with the long QT syndrome, who have greatly enhanced ~ y to ' ' released by the nervous sysoem. C. Day, et al., "QT dispersion: an indication of arrhythmia risk in patients with long QT
intervals," l~r. Heart J., vol. 63, pp. 342-344 (1990). These ~.,~ Liull were confirmed and exoended in C. Napolitano, et al., "Dispersion of a marker of successful therapy in long QT syndrome patients [abstract]," Eur. Heart J., vol. 13, p. 345 (1992).
The present inYentors' ~ 1 studies have ' ' that the variance of T-wave dispersion in the epicardial ~ u~ exhibits a highly significant predictive value in estimating risk for ventricular fibrillation during acuoe myocardial ischemia. R. Verrier, e~ al., "Method of assessing dispersion of ~ ; -, during acuoe myocardial ischemia without cardiac electrical testing [abstract]," Circulanon, vol. 82, no. III, p.450 (1990).
Fu-;' , their data has ~ ' that a linear 1~ e~ists between the epicardial and the precordial ECG. See U.S. Pat. No. 5,148,812.
This provides the scientific basis for utilizing precordial T-wave dispersion asa measure of the degree of ll~,t~,luc~ ,;.y of ~ , which occurs within the heart.
Napolitano et al., supra, have shown in human subjects afflicoed with the long QT syndrome that the variance of QT inoerval in the six standard precordial leads of the ECG is more accuraoe than the limb leads in estimating }isk of life-threatening ~ . These il. ~ tOI ~ have also ~' ' that dispersion of QT interval also provided a marker of successful therapy in patients receiving beta-blockade therapy and those undergoing cervical ~ "' y.
Within the last year, it has been ~ ' that QT interval 30 dispersion can predict the d~v~,lu~.. l.,.lt of Torsades de Poinoes, a precursor arrhythmia to ventricular fibrillation in patients receiving ~IILidlllly~ , drug ~ 21 77~39 therapy. T. Hii, ef al., "Precordial QT inoerval dispersion as a marker of torsades de pointes: disparate effects of class la ~.,Li~llh~i' drugs arld ~I..;Od~lUIIC,'' Circulatfon, vol. 86, pp. 1376-1382 (1992).
Another method which has been explored to assess autonomic nervous system activity, the neural basis for vulnerability to sudden cardiac death, is analysis of heart rate variability (HRV). Heart rate variability, however, is not an absolute predictor of SCD because there are major, non-neural factors which contribuoe to sudden death. These include: coronary artery disease, heart failure, myopathies, drugs, caffeine, smoke, ~.IIV;IUIIIII.~IIL~I factors, and others. Accordingly, techniques which rely on heart rate variability to predict cardiac electrical stability are not reliable.
Further, CUl~ iUllal techniques for analyzing heart rate variability have relied on power spectrum analysis. See, for example, Glenn A. Myers et al., "Power spectral analysis of heart raoe variability in sudden cardiac death~ mrari~on to other methods," Ir~ Transactions on Biomedical rngineering~ vol. BME-33, No. 12, December 1986, pp. 1149-1156. As discussed above, however, power spectrum (Fourier) analysis averages time functions over an entire time series so that rapid ~IIllyLlllllo~ , changes are not detected.
Complex ~IPrnn~ n as a method for analyzing heart rate variability is discussed in Shin ef al., "Assessment of autonomic regulation of heart rate variability by the method of complex ~' "~ "" rEr~E Transactions on Biomedical l~ngineering, vol. 36, No. 2, February 1989, which is ;III,UII~ ' herein by reference. Shin et al. teach a method of evaluating the influence of autonomic nervous system activity during behavioral stress. A technique of complex ~ ~ ' ' is used to analyze the patoern of beat-to-beat inoervals to deoermine the relative activity of the ~yllllJa~ Li~. and I~lG~ylll~ai~ Li~
nervous sysoems. While Shin et al. exploited the dynamic analytical .. 1,"".. ~ ;. c of complex .1. ~ -, they did not relate their results to cardiac vulnerability.

WO 95/15116 2 i ~ 7~ 3 ~ PCI/[iS94/13736 Similarly, T. Kiauta et al. ~Complex ~ n~ -- of heart rate changes during orthostatic testing," r~v~J;~ Computers in Cardiology, (Cat. No. 90CH3011 'L), IEEE Computer Society Press, 1991, pp. 159-162, discusses the use of complex ~ to assess heart rate variability induced by the standing-up motion in young healthy subjects. Using the technique of complex ~l, .,..vi.ll-l;..,, Kiauta et al. conclude that the complex ,iPm~-~ of the high frequency band probably refleets l~ala~ylll~aLII~
activity, but the complex ~' ' ' of the low frequeney band does not seem to indicate by~ JaL}~,iic aetivity. Similar to Shin et al., Kiauta et al. do notrelate their results to cardiac ~ulll~,.ab;lily.
In summary, analysis of the IllUl~llUlo~y of an ECG (i.e., T-wave alterrians and QT interval dispersion) has been recognized as a means for assessing cardiac ~ u~ alJ;liLy . Similarly, analysis of heart rate variability has been proposed as a means for assessing autonomie nervous system activity, the neural basis for cardiac vulnerability. When ICD.,al111il~ vulnerability to sudden cardiac death, researchers have cull~,.,iiullally relied on power speetrum (Fourier) analysis. However, power spectrum analysis is not capable of tracking many of the rapid allhy ' " changes which . l. --,.. ;.. T-wave alternans and dispersion and heart rate variability. As a result, a non-invasive diagnostic method of predicting vulnerability to sudden cardiae death by analysis of an ECG has not aehieved elinical use.
What is needed is a non-invasive, dynamie method for completely assessing vulnerability to ventrieular fibrillation under diverse pathologic eonditions relevant to the problem of sudden cardiae death. Among the most significant problems are enhanced discharge by the ~ylll~JaLll~,iic nervous system, behavioral stress, aeute myoeardial isehemia, reperfusion, effeets of r~ u~ agents on the autonomie nervous system, and intrinsie cardiac effects of ~,l,,... - ul.~y,ir agents. To ' these conditions, the method must not assume stationarity of data and must be sensitive to slowly varying amplitude and phase over time. The diâgnostie system must be sensitive to the faet that the area of injury to the heart ean vary j;6-.;rca~Lly, WO 95/15116 PCI/US94J~3736 ~ ~ J.~ 9 that extrinsic as well as intrinsic influences affect the electrical stability of the heart, and that tbe elL~,LIu~ olv~ic end point to be detected must be ~Iy linked to cardiac vul~ L;liLy.
SUMMARY OF THE INVENTION
The present invention is a method and apparatus for non-invasive, dynamic tracking and diagnosing of cardiac vulnerability to ventricular fibrillation. lt is non-invasive as it detects vulnerability from leads placed on the surface of the chest. Tracking and diagnosis of cardiac electrical stabilityare achieved through ~il""ll-,...,..- assessment of T-wave alternans, QT
interval dispersion, and heart rate variability. The method permits tracking of transient but deadly ~Jallu~lly~;ùlO~ ;c events, such as enhanced discharge by the ~y~ JaLh~,L;C nervous system, behavioral stress, acute myocardial ischemia and reperfusion.
T-wave alternans, heart rate variability and QT interval dispersion are ' '!/ evaluated. T-wave alternation is an excellent predictor (high sensitivity) of cardiac electrical instability but can be influenced by mechano-electrical coupling which does not influence cardiac v, ' ' li-y but reduces the specificity of the measure. QT interval dispersion is a less accurate predictor (lower sensitivity) of cardiac electrical instability but is not sensitive to mechano-electrical coupling. However, potential artifacts may be generated by eA~.c;,,;v~,ly low heart rate in QT interval dispersion or by its use of multiple leads. Heart rate variability is a measure of autonomic influence, a major factor in triggering cardiac _IIllyLlll.l;a~. By ~ u~ly analyzing each ~1,~ .,....~..,~,-- (T-wave alternans, QT interval dispersion and heart rate variability), the extent and cause of cardiac vulnerability can be assessed.
This has important IAI ;r~ ;-",~ for tailoring and assessing the efficacy of drug therapy.
The method includes the following steps. A heart is monitored to sense an ECG signal. The sensed ECG signal is then amplified and low-pass filtercd before it is digitally sampled and stored. Estimation of alternans amplitude W0 95/1~116 ~ 3 ~ PCT/US94/13736 and extent of dispersion and analysis of heart rate variability are then separately performed.
Estimation of the amplitude of alternans is performed as follows. The location of the T-wave in each R-R interval (heart beat) of the ECG is estimated, and each T-wave is partitioned into a plurality of time divisions.
The sampled ECG signal in each of the time divisions is summed together and a time series is formed for each of the time divisions such that each time series includes f~",.~l..",.l;"~ time divisions from successive T-waves. The time series are detrended before further processing in order to remove the effects of drift and DC bias.
Dynamic estimation is performed on each time series to estimate the amplitude of alternation for each time division. The preferred method of dynamic estimation is Complex D- ~n~ " Other methods include Estimation by S~lhtr~rtinn~ Least Squares F Auto Regressive Estimation, and Auto Regressive Moving Average Estimation. The amplitude of alternation is used as an indication of cardiac CllC~`~rtihility to ventricular fibrillation (i.e., cardiac electrical instability).
Estimation of a measure of QT interval dispersion is performed by analyzing ECG signals taken from a plurality of electrode sites. Dispersion is determined by analyzing the ECG signals across the electrode sites. In the preferred .. ,.1.~.1;,.. ~ one of five diffeRnt methods may be used to estimate a dispersion measure. First, dispersion may be computed as a maximum difference between QT intervals taken across the plurality of electrode sites.
Second, dispersion may be computed as a maximum difference between QT
intervals which have been corrected using Bazett's formula. Third, dispersion may be estimated by a method which takes the standard deviation of a QT
interval ratio. Fourth, dispersion may be estimated by a method which takes the standard deviation of the corrected QT interval ratio. Finally, dispersion may be estimated by computing the maximum RMS (root mean square) deviation of the ECG waveforms recorded from a plurality of sites.

wogs/lsllF 2 1 7 7 ~ 3 q PCTNS94113736 Analysis of heart rate variability is performed as follows. The apex of each R-wave is l~'t~'nnim'rl, and the time between successive R-waves is computed to deterrnine a magnitude (time) of each R-R interval. The magnitude of each R-R interval is then compared to a L~lc '~ ' crioerion S to eliminate premature beats. Ne~t, a time series of the ~ ' of the R-R intervals is formed. Dynamic estimation is performed on the time series to estimate the magnitude of a high frequency component of heart rate variability and to estimate the magnitude of a low frequency component of heart rate variability.
The magnitude of the high frequency component of heart rate variability is indicative of ~ y~ Lll~ , activity. The magnitude of the low frequency component of heart rate variability is indicative of combined ~yl~ ,.i., activity and ~ a,y~ Lh~, activity. A ratio of the low frequency component and the high frequency component of heart rate 1~ variability is formed. The ratio is indicative of ~ylll~ .ic activity or vagal withdrawal. In addition, recent studies have shown that particular emphasis should be paid to the Very Low Frequency (VLF) (0.0033 to 0.04 Hz) and Ultra Low Frequency (ULF) (<0.0033 Hz) spectral portions of heart rate variability as a powerful predictor of arrhythmia in the first two years following a myocardial infarction.
In the preferred I .,.1.-,.1;",. .1l of the invention, the ECG is sensed non-invasively via the precordial or chest leads for optimal alternans detection.
Leads V5 and/or V6 detect the optimal alternans signal when the left side (the most common site of injury for the ~JlU~ ,GLiUII of life-threatening ~IIIy ' of the heart is ischemic or injured. Leads Vl and/or V2 are optimal for detecting obstruction of the right-sided coronary circulation. Additional precordial leads, such as V9, may be useful for sensing alternans resulting from remote posterior wall injury. A physician may use the complete precordial lead system to obtain precise i ,. '~ .. ." -' i. ~,. non-invasively regarding the locus of ischemia or injury.

WO 95/15116 ;~ 7 ~ 3 ~ PCT/US94113736 For the dispersion measure, a plurality of chest leads (e.g., the standard precordial or some greater number) may be used to provide a plurality of electrode sites across which dispersion may be measured. Heart rate variability is easily sensed from any of the standard ECG leads.
The foregoing and other objects, features and advantages of the invention will be apparent from the following, more particular description of a preferred ~ ~ ~ ' to the invention, as illustrated in the ~U
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. lA is a typical ECG plot.
FrG. lB is a typical ECG plot and action potential plot illustrating the correlation between dispersion of ~ and the QT interval.
FrG. lC shows a number of heart rate plots with c spectral plots.
lS FrG. 2A is high-level block diagram illustrating the diagnostic principles of the present invention.
FIG. 2B is a block diagram illustrating the diagnostic principles of the present invention in a first example.
FrG. 2C is high-level block diagram illustrating the diagnostic principles of the present invention in a second example.
FrG. 3 is a flow chart illustrating the method of the present invention.
FIG. 4 is a flow chart detailing the process of dynamically estimating the amplitude of T-wave alternans (as performed in step 314 of FIG. 3).
FIG. SA is a flow chart detailing the process of dynamically analyzing heart rate variability to determine the activity of the autonomic nervous system(as performed in step 314 of FIG. 3).
FIG. SB is a flow chart detailing the process of dynamically analyzing heart rate variability to determine the ultra low and very low frequency activity of the autonomic nervous system (as performed in step 314 of FIG.
3)-Wo 95/15116 Pcr/uss4J~3736 2 ~ 778~i9 FIG. 6 is a flow chart illustrating a method for estimating first and second measures of QT interval dispersion.
FIGS. 7A and 7B is a flow chart illustrating a method for estimating third and fourth measures of QT interval dispersion.
FIG. 8 is a flow chart illustrating a method for estimating a fifth measure of QT interval dispersion.
FIG. 9A is a high-level block diagram of the apparatus of the invention.
FIG. 9B is a detailed block diagram of ECG detector and pre-processor 902.
FIG. 9C is a detailed block diagram of ECG processing system 904 comprising a ~ ,lu~,ul. ~
FIG. 10 is a detailed block diagram of the preferred ~ "l~ of the heart monitoring unit (HMU) 900.
FIG. 1 lA is an ECG recorded within the left ventricle of a dog before coronary artery occlusion as set forth in the animal study below.
FIG. llBshows~ of sixsuccessivebeatsfromFIG. llA
presented on an expanded time scale.
FIG. 12A is an ECG recorded within the left ventricle of a dog after four minutes of coronary artery occlusion as set forth in the animal study below.
FIG. 12B shows ~ of six successive beats from FIG. 12A
presented on an expanded time scale.
FIG. 13A is an ECG recorded within the left ventricle of a dog after release of the coronary artery occlusion (during reperfusion) as set forth in the animal study below.
FIG. 13B shows ~ iu.. of six successive beats from FIG. 13A
presented on an expanded time scale.
FIG. 14A is a surface plot of the T-wave oF the ECG for eight dogs with intact cardiac innervation showing the effects of coronary artery occlusionand reperfusion.

WO 95115116 2 ~ 7 7 8 3 9 PCIIUS94/13736 FIG. 14B is a surface plot of the T-wave of the ECG for six dogs after bilateral stellectomy showing the effects of coronary artery occlusion and .cl,~,.r FIG. 14C is a surface plot of the T-wave of the ECG for eleven dogs during thirty seconds of stimulation of the ansa subclavia of the ~l . . .,1.,.1i ;1 left stellate ganglion showing the effects of coronary artery occlusion and IC~
FIG. 15 shows the correlation between the occurrence of -r ventricular fibrillation and T-wave alternans in ten dogs.
FIG. 16 is a graph showing the responses of the ~y . ' and yl~ ih~,~ic nervous systems to a LAD coronary artery occlusion and reperfusion as indicated by heart rate variability.
FIGS. 17A-17C illustrate the positioning of the precordial ECG leads on the body.
FIG. 18 is a cross-section of the human body illustrating the positioning of precordial ECG leads V,-V6 relative to the heart.
FIG. l9A is an ECG recorded from lead Il during coronary artery occlusion in a dog.
FIG. 1 9B shows ~ of six successive beats from FlG . l 9A
presented on an expanded time scale.
FIG. 20A is an ECG from precordial lead V5 recorded ~ r ~ y with the ECG of FIG. l9A.
FIG. 20B shows ~ of six successive beats from FIG. 20A
presented on an expanded time scale.
FIG. 21A is an ECG from a left ventricular illLI~c~lviLdly electrode recorded cim~ / with the ECG of FIG. l9A.
FlG.21Bshows~ i-- ofsixsuccessivebeatsfromFlG.21A
presented on an expanded time scale.
FIG. 22 is a graph showing the relative magnitudes of alternans signals sensed from lead 11, from precordial lead V5, and from a left ventricular illLIcl~viLdly electrode.

WO95/15116 2 ~ 7 18 ~ 9 PCT/US94/I373Ij FIG. 23 is a surface plot display obtained by the method of complex ' ' (as set forth above) of the T-wave of the V4 precordial lead during ~ heart rhythm in a r~ , patient during ~ J.
FIG. 24 shows the level of T-wave alternans as a function of recording S site in seven patients at three minutes of: ., .' !/-induced occlusion and upon balloon deflation.
FIG. 25A and 25B illustrate an example positioning of a plurality of ECG leads on the body for QT dispersion ~ t.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
INTRODUCTION
The invention is directed to a method and apparatus for screening individuals at risk for sudden cardiac death. In order to produce an optimal testing .". :I..~nlnr,y, the invention takes a receiver operating ~1 - ", l. .;~1;, (ROC) curve approach to cardiac risk ~ r~ The invention meets three criteria required for successful risk !71.,liri -l;~.. and treatment:
(I) i.l..,liri.-li.... of subsets of patients at high risk for sudden cardiac death;
(2) elucidation of specific ' by which sudden cardiac death occurs; and 20 (3) i~lFntifir:~tif~n of ~,. l,.. ,: .. ~ at which treatment can be aimed.
The following terms are used herein:
Complex ;' ' A spectral analysis method which estimates the amount of signal in a specified frequency band by frequency translation of the signal and low-pass filtering.
Expert system: A domain-specific (e.e., medicine, F .. ~,;1.. ;.lp" ~rr-ol~ntin~) - computer system built to emulate the reasoning process of the mind of an expert in that domain.

2 ~ ~783~ --neart rate ~.. ' ' ~.~. An estimate of the frequency content of variation inheart rate as a measure of automatic nervous system output.
~I~. .lidl infarction: Damage to or death of cardiac muscle, usually due to coronary artery occlusion as a result of plaque rupture or formation of a clot.
Negative I ~ . The probability that an individual is truly disease-free given a negative screening test. It is calculated by dividing the number of truenegatives by the sum of false negatives and true negatives.
Neural net~ork: A computing model which emulates to some degree the cll~,l.;k~ and function of a group of neurons. The network is trained to interpret input data by adaptive adjustment of the strength of the Positi~e y~ . The probability that a person actually has the disease given that he or she tests positive. It is calculated by dividing the number of 1~ true positives by the sum of true positives and false positives.
1~ 1 '- . il~ . The probability that an individual actually has the disease, given the results of the screening test.
S. ~ili . il~. The probability of testing positive if the disease is truly present.
It is calculated by dividing the number of true positives by the sum of true positives and false negatives. True positives are the individuals for whom the screening test is positive and the individual actually has the disease. False negatives are the number for whom the screening test is negative but the individual does have the disease.

WO95/15116 2 1 ~ 7 8 3 9 pcrAJss4ll3736 S~ . The probability of screening negative if the disease is truly absent. It is calculated by dividing the number of true negatives by the sum of false positives and true negatives. True negatives are individuals for whom the screening test is negative and the individual does not have the disease.
False positives are the individuals for whom the screening test is positive but the individual does not have the disease.
Sudden cardiac death: Natural death due to cardiæ causes, heralded by abrupt loss of . within one hour of onset of acute symptoms, in an individual with or without known preexisting heart disease, but in whom the time and mode of death are llnp~rcrtp~ Sudden death is the leading form of adult mortality in the industrially developed world, claiming one death per minute in the United States alone. Coronary care unit and out-of-hospital '-"`' .1..l;~.ll experience have shown that sudden death is due primarily to ventricular fibrillation.
T-wave alternans: A regular beat-to-beat variation of the T-wave of an uudldio~-all- which repeats itself every two beats and has been linked to underlying cardiac electrical instability.
The preferred ' ' of the invention is discussed in detail below.
While specific cf~nfiellt~ti~n~ and ~ are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the art will recognize that other ~ and ,.~ may be used without departing from the spirit and scope of the invention.
The preferred ~..,1..,.1;1,,.: of the invention is now described with reference to the figures where like reference numbers indicate like elements.
Also in the figures, the left most digit of each reference number CUII~,~U
- to the figure in which the reference number is first used.
Figure lA shows a l~ .lLa~ive human surface ECG 100. A
deflection 102 is known as the "P-wave" and is due to excitation of the atria.

2 ~ ~78~ --Deflections 104, 106 and 108 are known as the "Q-wave, " "R-wave, r and "S-wave, " respectively, and result from excitation (de-pol~ll ;~tiUI~) of the ventricles. Deflection 110 is known as the "T-wave" and is due to recovery (~r~ ) of the ventricles. One cycle (i.e., cardiac cycle or heart bcat) of the ECG from the apex of a first R-wave to the apex of the next R-wave is known as the R-R or interbeat interval. Heart rate variability (HR~) refers to changes in the heart rate (HR) or length (time) of the interbcat interval from one bcat to the next.
A portion 112 between S-wave 108 and T-wave 110 of ECG 100 is known as the "ST segment". ST segment 112 includes the portion of the ECG
from the end of S-wave 108 to the beginning of the T-wave 110. Because this invention is concerncd with alternans in the ST segment as well as in the T-wave, the term rT-wave" in this disclosure, as noted above, includes both the T-wave and the ST segment portions of the ECG. The inventors have found that most alterrlation occurs in the first half of the T-wave, the period of greatest vulnerability to ventricular fibrillation. See, Ncaring BD, Huang AH
and Verrier RL, "Dynamic Tracking of Cardiac Vulnerability by Complex D ,~ ;u.. of the T Wave," Science 252:437-440, 1991.
This invention is also concerncd with the QT interval. The QT interval is defined as the period between the beginning of the Q-wave and the end of the T-wave. However, other definitions for the QT inoerval (e.g., from the beginning of the Q-wave to the apex of the T-wave) may be used without departing from the spirit and scope of the invention as defined in the claims.
Figure lB illustrates the concept of QT interval dispersion. A sample ECG signal 150 and a cu,-c r ~ " cellular action potential 160 are shown.
Line 152 indicates the beginning of the Q-wave. Line 154 indicates the end of the T-wave. Action potential 160 represents the cellular ~
occurring during the QT interval 156. Note that dispersion 158 occurs primarily during the first half of the T-wave as illustrated between lines 162,164. This is the period in which the hcart is most vulnerable to cardiac electrical instability.

WO 95/15116 2 1 ~ 7 ~ ~ q PCI~/US94/13736 A more detailed discussion of ECG sensing and analysis is provided in Dale Dubin, Rapid l~.'LI~I ' ' '~n ~f EKG's, 4~ Edition, Cover Publishing Company, 1990, which is i r ' ~ herein by reference.
Conventionally, autonomic nervous system activity, as indicated by S heart rate variability, has been researched as an; ~ indicator of cardiac VUIIl.,l~;lily (electrical stability). Autonomic nervous system activity, however, is not an absolute predictor of cardiac vulll.,l~;liLy.
Further, LUllV.,~.~iUI~I research has evaiuated heart rate variability, ECG , ' ~' Oy as indicated by T-wave alternans, and ECG l~lu,~llolv~;y as indicated by QT interval dispersion as i,.. l.1,.. ,.1.. ,l variables indicative of cardiac vulnerability. This also is an invalid ~Ccllmrrinn HRV and ECG
~"u~holuOy are linked, however, not invariably. Alternans, QT interval dispersion and HRV can each change i~
Heart rate variability and ECG . ' ' "y measure different aspects of ~,~ i;uv~ ,ulal control. Both must be assessed in order to fully diagnose cardiac ~L Il~,l~ili~y. The inventors have discovered thaî
analysis of heart rate variability, T-wave alternans and dispersion yields important diagnostic i.,r~.. -~;.". pertaining to cardiac VUII.. ,I~ili~y.
Heretofore, this i"r.", ;~ has not been available.
20 By "~i"",ll~,.. ~", it is meant that the analysis of T-wave alternans, dispersion and heart rate variability is carried out on the same ECG data. It is not necessary for this to be done at the same time. For example, the ECG
data may be stored and the individual analyses performed in sequence one after the other.
2~i Cardiac vulnerability is affected by both intrinsic and extrinsic factors.
The intrinsic factors include coronary artery occlusion and l,dl iiUlll,yO~ ily.The extrinsic factors include the autonomic nervous system, ~I~,.""~ Oic agents, body chemistry (e.g., el~ ul~), and other chemicals (e.g., from - cigarette smoke, caffeine, etcetera).
An intrinsic factor can make a heart electrically unstabie and therefore susceptible to SCD. T-wave alternans and dispersion are indicative of cardiac WO95/15116 2 ~ PCTIUS94/13736 electrical instability caused by intrinsic factors. Without T-wave alternans, a heart is not at risk of sudden cardiac death (~ ,uldl fibrillation). As the magnitude of aloernans increases, so does the risk of sudden cardiac death.
T-wave aloernation is an excellent predictor of cardiac electrical stability but can be influenced by mechano-electrical coupling. Alternans measures both excitable stimulus and ll~ ob~ ;t~ of Ir~ ., of the cardiac substraoe. It is an intrinsic property of an ischemic and reperfused lll.v~/~ld;....l. However, mechano-electrical coupling (e.g., through pericardial effusion and tamponade, abrupt changes in cycle length, drugs, and the like) which does not have an influence on cardiac ~ / will influence aloernation. Thus, a measure of alternation has a high degree of sensitivity buta low degree of specificity.
The inventors have discovered, however, that the low specificity of aloernation can be addressed using a test which ' '~/ analyzes another variable, QT interval dispersion. Dispersion is not a measure of excitable stimulus and is not sensitive to mechano-electrical coupling.
However, its specificity is reduced in cases of low heart rate and due to its l~iU,U;lt;ll.~ of multiple leads. The resulting cc." l ;~ - of aloernans and dispersion yields an accuraoe predictor of cardiac electrical instability causedby intrinsic factors.
Extrinsic factors may also cause or increase the electrical instability of the heart by causing or increasing aloernans and dispersion. The autonomic nervous sysoem is a primary extrinsic factor which affects cardiac electrical stability. Relative changes in actions of the ~ ylllpGLll~ sysoem versus the ,~ ,iic sySoem can increase the magnitude of alternans, resulting in an increased vulnerability to SCD. However, a change in the autonomic nervous system by itself is not an absolute cause or predictor of cardiac electrical instability.
Heart rate variability is a measure of autonomic nervous system function. Generally, decreased heart rate variability will tend to increase the magnitude of aloernans. Further, as described in detail below, analysis of the WO95/1~116 2 1 ~ PCrlUSs4/13736 spectral content of heart rate variability indicates that the high frequency (e.g., 0.354 Hz) portion of the signal Wll~ Jlld~ to ~ Oylll~lLh~ (i.e., vagal) activity while the low frequency (e.g., 0.08 Hz) portion of the signal t ~ - . ' to combined ~y~ ;c and ~ y~ Ja~ Lt~ activity.
A detailed discussion of heart rate modulation by the autonomic nenous system is provided in J. Philip Saul, "Beat-to-beat variations of heart rate reflect modulation of cardiac autonomic outflow, " News in r~yA ;I7~0gi~ul Sciences, vol. 5, February 1990, pp. 32-36.
Referring to Figure IC (reproduced from Id. at pûge 35), Saul shows the heart rates and ~", ~ frequency spectra 120 for a patient with a normal heart, 122 for a patient with congestive heart failure, 124 for a diabetic patient with a peripheral neuropathy, 126 for a diabetic patient with a cardiac autonomic neuropathy, 128 for a patient with a 1~.-"~ 1 heart pnor to re-innervation, and 130 for a patient with a i , ' ' heart after re-innervation. As can be seen from inspection of these data plots, the loss of neural activity either due to diabetes or cardiac transplant is evident in the absence of normal spectra. With return of normal innervation, the spectra at least partially return.
Figure 2A is a block diagram illustrating the diagnostic principles of the present invention. Block 202 represents all factors which affect the electrical function of the heart (e.g., drugs and/or diseases). Block 204 represents increased heart rate variability resulting from the factors of block 202. Block 206 represents alternation of the amplitude of the T-wave and dispersion of the QT interval resulting from the factors of block 202. Block 208 represents sudden cardiac death resulting from ventricular fibrillation.
As shown, the factors of block 202 can lead to SCD in block 208 by two major pathways. The first pathway is from block 202, through block 206, to block 208. This results from a direct influence of the factors of block 202 - on the electrical stability of the heart, manifest in the form of T-wave alternans and QT interval dispersion This mode of SCD would occur without a change in heart rate variability because the nervous system is not involved A
. .

WO 95/15116 ~ 8 ~ ~ PCI/llS94/13736 corollary to this is that a sudden death prediction method which relies solely on heart rate variability would not be adequate to detect SCD.
The second major pathway from the factors of block 202 to SCD in block 208 is through blocks 204 and 206. This results from an influence of the factors of block 202 on the autonomic nervous system. Drugs or heart disease, for example, can ~;6llir~ 1y alter neural activity. This will be expressed as changed heart rate variability. Certain changes in neural activity which increase ~ylll,u~L~ , tone ~;6lliG~l-lly increase T-wave alternans and QT interval dispersion and therefore could result in SCD.
The inventors have discovered that by combining an indication of heart rate variability with an indication of either T-wave alternans or QT interval dispersion, it is possible, not only to assess risk for SCD accurately, but alsoto determine whether a ~ in autonomic nenous system activity is causal. This has important clinical c~ as it affects both diagnosis and therapy. In the preferred ~ ' ~o~l; 1 ' both T-wave alternans and QT interval dispersion are analyzed in C.~.-J.-II. 1;~.,1 with heart rate variability.
For example, terfenadine (Seldane) is a drug widely employed for the treatment of sinus problems. It has recently been discovered that, when terfenadine is used in ~ with antibiotics, SCD can result.
Terfenadine has no known effects on the autonomic nervous system and ~.,...I ...,lly does not affect heart rate variability. However, the drug can result in alternans and torsades de pointes in isolated heart ~ICIU " and is thus capable of directly de-stabilizing the electrical activity of the heart.The 1~ of T-wave alternans and/or QT interval dispersion is therefore an essential approach to detect s~crf~rtihility to SCD induced by a dill~ llLil,iu~;1 ,~,1l,1,;. -l;.." This is illustrated in Figure 2B.
For another example, digitalis drugs are the most commonly used agent for increasing the strength of contraction of diseased hearts. The drugs produce this effect by both direct influence on the heart and through alterations in the autonomic nervous system. In the proper therapeutic range, there is no significant negative effect on the electrical stability of the heart. However, WO95/15116 ? ~ 7~ ~3 9 PCT~US94/13736 when the dose is either too high or the patient's health status changes due to illness, the same dose of drug may become toxic. It is often difficult to determine whether a patient is under-dosed or overdosed. By using a combined alternans/dispersion/HRV analysis, it would be possible to determine at what point a neurotoxic influence may lead to alternans and SCD. In particular, high doses of digitalis decrease vagal tone and increase sy~ Lh,ii~,activity, effects which would be clearly detected in an heart rate variability analysis. This is illustrated in Figure 2C. This ;,lr.,""-~i.", would be a valuable asset in the therapeutic ~ of the patient.
As discussed above, traditional methods of quantifying hcart rate variability or the magnitude of alternans have relied on power spectrum (Fourier) analysis. However, power spectrum arlalysis is not capable of tracking many of the rapid ~ lyilllllG~ ic changes which ~ . ;,. T-wave alternans and heart rate variability. In the preferred '.IJ~I;lll. '~, the present invention utilizes complex ~' ' to analyze heart rate variability and T-wave alternans.
METHOD OF THE INVENTION
The method of the present invention for analyzing an ECG is now discussed with reference to Figures 3-8.
An ECG signal containing a plurality N of R-R intenals is sensed from a patient in real time at step 302. For alternans and heart rate variability analysis, only a single ECG signal (i.e., an ECG signal sensed from a single site) is required. For dispersion analysis, however, a plurality of ECG signals (i.e., ECG signals sensed from a plurality of sites) are required. The preferred method of non-invasively sensing the ECG signals is discussed in detail below. Because the body is akin to a dipole, a large DC component will be present in the sensed ECGs. This DC component is removed at step 304 - with a high-pass filter prior to ~mrlifir~ of the I~CG signals at step 306.
The amplified ECG signals are then low-pass filtered at step 308 to limit the signal bandwidth before they are digitally sampled at step 310. The digitized WO95/lS116 2~ 7~ PCT/US94/13736 data may then be stored on a magnetic or optical storage device at step 312.
Finally, the digitized ECG data is processed or analyzed at step 314.
Processing at step 314 involves: (1) producing an estimation of alternans amplitude, (2) estimating the magnitude of discrete spectral --- r of heart rate variability to determine the ,y~ "i., and ,ylll~clih~,(J~, influences on cardiac electrical stability, and (3) the extend of QT interval dispersion.
As an alternative to this real-time signal pre-processing, the ECG
signals may be retrieved from the storage device (step 312) and processed (step 314) at a later, more convenient time. Processing/analyzing step 314 involves three i~ u"~ alternans processing, heart rate va}iability processing, and QT interval dispersion processing. Each is discussed in detail below.
T-WAVE ALTERNANS
The analysis of alternans at step 314 is described in detail with reference to Figure 4. At step 404, the apex of each R-wave in the signal data for each of the N beats is located by finding the peak amplitudes in the digitized signal. Premature beats are removed at step 406 by rJ '" 1"" ;`- "' ofeach R-R interval with fixed criteria. At step 408, a portion of the ECG
~1 ~l l r~ l(1; l Ig to an estimated location (with respect to R-wave 106) of T-wave 110 is identified.
At step 410~ the T-wave 110 and 112 portion of the ECG signal is partitioned into "B~ time divisions, where "B" may include a single digital sample or a plurality of samples. The area between the ECG and the isoelectric baseline is computed for each time division, at step 412, by summing the areas of all samples in the time division. Then at step 414, "N"
successive beats (e.g., from control through release in the animal ~ Al.. .; 111. ..`~
discussed below) are sequenced into a time series for each of the "B" time divisions: (X(n), n = 1,2,...N).

WO 95/15116 2 1 7 7 ~ 3 9 PCrlUS94JI3736 A high-pass filter is used for detrending the time series at step 416 to remove the effects of drift and DC bias (e.g., high-pass filtering removes the large low-frequency variation in T-wave area that occurs during occlusion of a coronary artery). A cleaner signal is then available for dynamic estimation, which is performed at step 418 to estimate the amplitude of alternation for each time series.
The estimation of step 418 may be performed via severai dynamic methods. By "dynamic" method, it is meant any analytical process sufficientiy rapid to track (i.e., estimate) transient changes such as those which occur in alternans amplitude in response to ~ ya;Ol~ic and ~ Jpl~ya;~lu~i1 processes triggering ~Illlly~ ..a. These include, for example, enhanced neural discharge, acute myocardial ischemia and Ic~,l rl A "dynamic" method should be able to track alternans from as few as d~ 'y ten heart beats (or less). This precludes analytic processes (e.g., Fourier power spectrum analysis) which require stationarity of data for several minutes. Specific, but not exclusive, examples of methods for dynamic estimation include:
(a) Complex D~mr,~i lqti-.n (b) Estimation by Sllhtrlrtinn (c) Least Squares Estimation, (d) Auto-Regressive (AR) F and (e) Auto-Regressive Moving Average (ARMA) Fctin~ ir n (A) COMPLEX DEMODULATION
Complex .i. ,..~.I..I-li.... is the preferred method of dynamic estimation of the beat-to-beat alternation in the amplitude of each time series. Complex .1.. ~ .. is a type of harmonic analysis which provides a continuous measure of the amplitude and phase of an oscillation with slowly changing amplitude and phase. It detects features that might be missed or Ill;alc~ ll~d by standard Fourier spectral analysis methods which assume stationarity of data.
By definition, alternans is a periodic alternation in the T-wave. The magnitude of alternans, however, changes slowly during a coronary artery W09511S116 ~ ~ 7 7 8 ~ ~ ~CTIUS94/13736 occlusion and more rapidly during release, making it quasi-periodic. As such, it must be represented by a sinusoid with slowly varying amplitude, A(n), and phase, ~(n):
X(n) = A(n) Cos[2~tf~LT + (p(n)] Eq. (1) where: X(n)= the data sequence with alterrlation in its amplitude f~LT = ~ alternation frequency (E~z). It should be noted that this frequency is half of the heart rate.
Using the identity cos(x) = C ~ , Eq. (2) the equation for X(n) can be rewritten as X(n) = A(n) x (e ej~ + e I Df~ e j~n) Eq (3) The method of complex ~ " requires ~ lyill~ this time series X(n) by two times a complex eYr~nPnr~ at the alternans frequency [to produce Y,(n)] and then filtering the result to retain only the low frequency term Y2(n) as follows:
Yl(n) = ~(n) x 2e i2i'f~
= A(n) [el~n~ + ~ JA~a~ -1~1 Eq. (4) Y2(n) = A(n) ~ ) Eq. (S) The amplitude and phase of the alternans is then found from the filtered signal, Y2(n), as follows:
where: Im and Re refer to the imaginary and real parts of Y~

WO95/15116 2 t ~ PCr/US94/13~36 A(n) = I Y2(n) 1 ç = magnihule of Y2(n) Eq. (6) = JRerY2(n)]2 + Im[Y2(n)]2 ~4(n) = p)u~se of Y2(n) a ta~lm[Y2(n)]l ~q- (n LRe[Y2(n)]~
For a more detailed discussion of complex f- ~~ ' ' see FoKrier An~lysis of Time Series: An In~u.~iu,~, by Peter PIo- mfil-ltl John Wiley &
Sons: New York, pp. 118-150: which is illco~l~ul~l~cd herein by reference.
(B) ESTIMATION BY SUBT~ACTION
The subtraction method of dynamic estimation is an alternative which may be substiwoed for complex ~l~mr~ llqri~n The subtraction method involves subtracting the area of each time division (n) of an R-to-R interyal from the area of the W~ p~Jlld;ll~ time division of a subsequent (n + 1), or alternatively, a previous (n-l) R-to-R interval to form a new time series Y(n) IC~ >CIILill~ Lhe magnitude of aloernans. Because this difference series Y(n) may be positive or negat~ve, the absoluoe value or magnitude of Y(n) is used for the magnitude A(n). That is:
Y(n) = X(n) - X(n - I) E~l. (8) A(n) = ¦ Y(n) = IX(n) - X(n-1)l Eq. (9) = magnitude of al~rnans Some errors may be introduced into this estimate due to the slowly varying increase in magnitude of the T-wave size at the start of a coronary occlusion and the reduction in size following the occlusion. Also, some T-wave variation due to respiration is expected. Therefore detrending the sequence X(n) using a high pass digital filoer, or equivalent, improves the WO 95/15116 2 ~ 7 7 Q ;~; ~ PCTIUS94/13736 .

estimate by removing the effects of T-wave size changes. Also, averaging M
samples together, where M is the number of beats occurring during a single respiratory cycle, aids in eliminating the respiratory effects on the estimate.
AlternatiYely, the digital filter may remove both trends and respiratory changesif tbe respiration frequency is sufficiently different from the heart rate, so that the filtering does not alter tbe magnitude of the alternans estimate.
(c~ LEAST SQUARES EISTIMATION
The least squares estimation, which also turns out, in this c~se, to be the maximum likelihood estimate for estimating sinusoid amplitude in white noise, is a second alternatiYe which may be substituted for complex ~rm~~ inn to calculate a new sequence which is a dynamic estimate of the amplitude of alternans. Least squares estimation of the amplitude of alternans A(n) for the data sequence X(n) is derived as follows.
Assume for M points (e.g., 5 to 10 cardiac cycles) tbat:
X(n) = A cos(2-rf"Lrn) + N(n) Eq. (10) where: N(n) represents additive noise In order to minimize the noise term and estimate the alternans cnmrn- nt create a new function T(A), where:
l~A) = ~ [X(~ - A Cos(2~fALr~]~ Eq. (11) T(A) represents a measure of the difference between the model and the dat~.
The best alternans magnitude estimate results if T(A) (i.e., the noise term) is minimiæd. To minimize T(A), take the derivative of T(A) with respect to A
and set it equal to zero:
Next, solve this equation for A(n) (shown simply as "A" above) and take the absolute value of the result to yield the least squares estimate of the magnitude W095/15116 ~ ~ ~783'~ PcrluS94113736 Eq. (12) oT = -2 x jl+M-1 lcos(2~fALr~ [X(~ - A cos(2~fA~ ]} =
of the alternans:
Eq. (13) A(n) = 1 ¦ ~j+M-I tX~ COS(21~fALJ~]¦
(D) AuTo-REG~EsslvE EST~ATION (AR) Auto-Regressive (AR) Estimation is a third method of dynamic estimation which may be substituted for complex ~ ;.," AR
5estimation models the alternans as follows:
Eq. (14) X(n) = ~ ~ [a(k) x X(n - k)] + u(n) In this model, "P" is the number of auto regressive L~Jrrr; ~ chosen for the estimation. u(n) represents noise and accounts for the imperfect fit of the estimation. The method of estimating the amplitude of alternans A(n) for the data sequence X(n) first involves calculating a matrix of co-variance 10~ ffi~ n+c c(i,k) according to the following formula:
Eq. (10 c(i,~) = M p j~=+~+pl [X(J - ~) x X(l - k)]
where: â r the best estimate of the true value of "a"
P = the number of auto regressive ~ "â"
M = the number of cardiac cycles The co-variance ~iue~;~ r~l~ are then used to form P" auto regressive , ~,rrri, :. .. l~ "â" as follows:
The estimate of the alternans magnitude is then given by:
For a more detailed discussion of auto-regressive estimation, see Modern Spectral Esh~nahon: Theory and Arrlirnrr~, by Steven Kay, WO95115116 2 ~ 7~83~ PCTIUSg4/13736 Eq. (1 â(l) c(1,1) c(1,2) ... c(l,P)-I c(1,0) â(2) c(2,1) c(2,2) .. c(2,1~) c(2,0) :
â(P) c(P,I) c(P,2) ... c(P,~) c(P,O) Eq. (17) a2 2(n) e ~~
where: a2 = c(0,0) + ~" I d(n) c(O,n) Prentice Hall, 1988, pp. 222-225; illl,UllJ~ ' ' herein by reference.
(E) AIJTo-REGREsslvE MOVING AVE~AG~ (ARMA) EsTn~ATIoN
Auto-Regressive Moving Average (ARMA) ~stimation is yet another dynamic method which may be substituted for complex r' ' ' ARMA
estimation involves modeling the alternans with a data sequence X(n) as follows:
Eq. (18) X(n) = - ~ I [a(k) x X(n - k)] + ~po [b(k) x u(n - ~)]
Note that this equation is similar to the model of X(n) according to the AR
method, however, additional coPffiriPnf~ "b(k)" have been added to the model.
These .u r~; ~ are necessary when the spectrum of the data has contours which are more complex than just spikes due to alternans and respiration Jrl~ Let "â and "6~ be the best estimates of "a" and "b". The auto regressive coefficient estimates are found by performing Newton Raphson Iteration to find the æros of:
This minimiæs the error function:

WO95/15116 2 ~ ~ 7 ~ ~ 9 PCT/US94/13736 Eq. (19) [( ~a ) ( ~b) ~
Eq. (20) Q(a,b) = ¦ ~2 I(fl 1~1~ df where~ ~-ol X(n) e~J2"f~¦2 A(f) = 1 - ~q, a(k) e -J2~k B(f) = ~=o b(k)e -~2~
The estimate of the alternans magnitude is then giYen by:
Eq. al) o2 ~I b(k) e~l2~fAa~
a(k)e where: a2 = Q( d"6 ) For a more detailed discussion of auto-regressive moYing aYerage estimation, see Modern Spectral F ` i~ Tfieory and ~pl;~r~` ~ns, by Steven Kay, Prentice Hall, 1988, pp. 309-312; illLUl~ herein by reference.
The resultant time series A(n), ~ of the magnitude of alternans, which is produced in step 418 (by one of the dynamic methods set forth aboYe), may then be anaiyæd for diagnostic purposes. This may include producing a surface plot as shown in Figures 14A-C (described below).
lt will be understood by one skilled in the art that the Yarious steps of filtering set forth aboYe may be performed by analog or digital means as discussed below. It will further be understood that each of the Yarious filtering steps may be modifled or eliminated from the method, if desired.

2 ~ 7 7 ~ ~ 9 PCTNS94113736 Note, however, that detrending is l~G~ U~ ly important for the Least Squares Estimate Method.
Flimir~linn of the various filtering steps will, of course, lead to a reduction in clarity and will add corruption to the sought after signals. The amount of corruption will depend on the amount of noise present in the specific data. The noise sources sought to be filtered include: white noise, respiration induced electrical activity, premature beats, slowly varying trends present in the area under the ECG waveforms, and other rnicrrl~ ollc noises.
HEART RATE VARIABILITY
The analysis of heart rate variability at step 314 is described in detail with reference to Figures SA and 5B. Referring first to Figure 5A, a first method of analysis is described. At step 504, the apex of each R-wave in the signal data for each of the N beats is located by finding the peak amplitudes in the digitized signal. At step 506, the R-R intervals (time) between successive R-waves is computed. Premature beats are then removed at step 508 by comparing each R-R interval with fixed criteria.
At step 510, a time series of R-R interval data is formed by listing the R-R interval times in order. At step 512, a second time series or sequence (Rt), whose points are 100 msec apart and whose values are the R-R intervals present at that time, is formed along the same time line. For example, if the R-R interval data for a certain ECG signal has the values:
300 msec, 350 msec, 400 msec then the series (Rt,t) would become:
(300,0), (300,100), (300,200), (350,300), (350,400), (350,500), (350,600), (400,700), (400,800), ~400,900), (400,1000) At step 514, the sequence (Rt) is filtered to remove any low frequency trends. A cleaner signal is then available for dynamic estimation, which is performed at steps 516 and 522 to estimate the magnitude of discrete spectral of heart rate to determine the ayllllJa~ and l)~tl~ylll~clLh~,,iL
influences on cardiac electrical stability. This dynamic estimation at steps 516 W095115116 2 1 ~ ~ 8~ 9 PCT/IJS94/13736 .

and 522 is performed using similar methods (except for Estimation by S '.tra~ti~n) to those discussed above with respect to analysis of alternans at step 418.
Specifically, the estimation at steps 516 and 522 may be performed Yia S Complex L ~ .. ,.~.1 1.~;"", Auto-Regressive (AR) F.cti~ri~ln Auto-Regressive Moving Average (ARMA) Fct;ln~ n, or other time domain methods.
Traditional power spectrum (Fourier) ana]ysis may be used, however, it is not 1 -1 because it will produce inferior results and some data (e.g., rapid changes in heart rate) may be lost.
Complex .I. ~ ;,-, is the preferred method of ~i "~ ;"~ heart rate variability. Complex ,I..,...I.,~ -:;.." of heart rate variability is performed as follows. At step 516, the sequence (R,) (from step 514) is multiplied by 2 e~J27r~, at f # 0.10 Hz to yield the low frequency component of heart rate variability. "n" is the index of the data point in sequence (R~). In parallel with the: . of the low frequency component of heart rate variability at step 516, the high frequency component of heart rate variability is computed at step 522 by IIlLlLilJly;llg the sequence (R,) by 2 e~2~), at f # 0.35 Hz (i.e., a frequency close to the respiration frequency). The low frequency component of heart rate variability is then low pass filtered (e.g., roll-off frequency 0.10 Hz) at step 518. The high frequency component of heart rate variability is low pass filtered (e.g., roll-off frequency # 0.15 Hz) at step 524. It should be noted that low pass filtering (steps 518 and 524) is part of the method of complex .1.. ~ (steps 516 and 522).
The magnitnde of the high frequency (e.g., # 0.35 Hz) component of heart rate is indicative of ~ ylll~JaLll~,ih, activity. The magnitude of the lowfrequency (e.g., ~ 0.10 Hz) component of heart rate, however, is affected by both ~ylll~Jaill~.~;c, and p~ ylll~cL~ L;~ activity. Therefore, to discern the influence of the ~y~ LII~.iC nervous system, the low frequency (LF) component of heart rate (from step 518) is divided by the high frequency (HF) component of heart rate (from step 524) at a step 520 to produce a ratio (LF/HF). This ratio is indicative of the ratio of ~ylll~.,LII~LiC activity to WO9S/15116 2~ ~$3~ P'`T/US94/13736 ,y~ ,Li-, activity and can thus be used to assess ~ .,.iC activity.
Ratioing low and high frequency . of heart rate to estimate ill.,,i., activity is further described in M. Pagani, et al., "Power spectral analysis of heart rate and arterial pressure variabilities as a marker of S sympatho-vagal interaction in man and conscious dog," C~rculanon Research, vol. 59, No. 2, August 1986, pp. 178-193, i ~ d herein by reference.
Steps 516,518 and 522,524 of the method described above detect heart rate variability using the method of complex ~ ;- Analysis of heart rate variability using the method of complex f~ is further described in Shin et al., discussed above.
Recently, there has been empirical evidence suggesting that particular emphasis should be paid to the Very Low Frequency (VLF) (0.0033 to 0.04 Hz) and Ultra Low Frequency (ULF) ( < 0.0033 Hz) spectral portion of heart raLe variability as a powerful predictor of arrhythmia in the first two years IS following a myocardial infarction. The basis for Lhe predictive value of there endpoints is uncertain, as VLF and ULF appear to reflect altered cardiac sensory input, neural efferent activity, cardiac Ic~u...,;~ , renin-angiotensin control, impaired baroreflex sensitivity and perhaps other factors.
See, for example, J. Bigger, et al., "Frequency Domain measures of heart period variability to assess risk late after myocardial infarction," J. Am. cOn.Cardiol., vol. 21, pp. 729-731(1993).
Thus, it may be desirable to also analyze the very low frequency and ultra low frequency ~- - 1-- , Il ~ of heart rate variability at least as an indicator of h.llulGf,l,~)Lul sensitivity. The method for estimating the magnitude of the VLF and ULF l .l ~ JI ~ of heart rate variability is described with reference to Figure SB. Steps 504-514 are identical to steps 504-514 of Figure SA.
Steps 526 and 532 are substantially the same as steps 516 and 522, IG~ ,ly, of Figure SB. That is, steps 526,532 estimate the amplitude of cerLain spectral l,u r ' of heart rate variability. These steps may be performed according to any of the methods previously described. However, WO95115116 2 l 77~3~ Pcr/uS~4113736 for simplicity, the steps are described using complex !' ' ' '- which is the preferred ~,111' ' ' At step 526, the sequence (R~ (from step 514) is multiplied by 2 e'~
~, at f ~ 0.00165 Hz to yield the ultra low frequency component of heart rate variability. In parallel with this ~ . 1, the very low frequency component of heart rate variability is computed at step 532 by multiplying the sequence (R~) by 2 e(l2~), at f ~ 0.022 Hz. The ultra low frequency component is low pass filtered (e.g., roll-off frequency ~ 0.00165 Hz) at step 528. The very low frequency component is low pass filtered (e.g., roll-off frequency--0.018 Hz) at step 534. It should be noted that low pass filtering (steps 528 and 534) is part of the method of complex d ~ ... (steps 526 and 532). Empirical evidence suggests that either the Very Low Frequency or the Ultra Low Frequency spectral portions of heart rate variability may be indicative of balule~ sensitivity, a powerful predictor of ;~ yi' Moreover, baroreflex sensitivity (gain) may be analyzed directly as an additional indicator of cardiac electrical stability The baroreflex sensitivity may be non-invasively, 1 ~, " ;, ~ as follows. First, an ECG signal, a signal indicative of arterial blood pressure, and a signal IClJll,~ll~ill~;
,.. v~ lung volume are digitized. The ECG signal may be processed in accordance with the method of Figure 3 prior to ~ itiJ~tir,n In addition, the peak amplitude for each R-R interval is determined to locate the apex of each R-wave and premature beats are removed. The R-R intervals may then be computed. Next, an ~ rv~ heart rate is computed for each R-R
interval.
An .. ~ vlcl lcaa;ve moving average model (discussed in detail above) is used to ~1,,..~ ;~ the present heart rate as a function of past heart rate, past lung volume, past arterial blood pressure plus a non-specific noise component using the following formula:
where: N, M and P represent the number of previous beats; and a, b and c represent the ARMA rol-ffiril-ntc The ARMA model is then used with the WO95/15116 2 t 7~3~ PCrn7S94113736 Eq. (22) N U
NRln) = [a(l`) x HR(n - I)] + ~, [b~j) x (b~ng vol~ime(n - ~)]
=l J l + ~, [c(k) x (BP(n - /c)] + noise ~-1 measured ECG, blood pressure and lung volume values to estimate values for the çorMriPntc a,b and c. The ~ r~ `~ can then be used to determine the baroreflex gain transfer function and the static and dynamic baroreflex gain.
(2T INTERVAL DISPE~SION
QT interval dispersion may be computed spatially (across a plurality of ECG leads) or temporally (across plurality of beats from a single ECG
signal). In the preferred i ' ~ " t, QT interval dispersion is computed both temporally and spatially. The dispersion is computed by analyzing the QT
interval across a series of electrode sites/signals. However, the beats from each ECG site/signal may be averaged prior to measuring the dispersion across several leads.
In the preferred ~ "~ a dispersion measure or estimation is computed using one of five methods. These methods are illustrated in Figures 6, 7A, ~B and 8 and described below. Referring first to Figure 6, a plurality N of ECG signals from N electrode sites are cim~ o-lcly digitized in a step 602. This step represents steps 302-310 of Figure 3. In a soep 604, the peak amplitude is determined for each R-R interval to locate the apex of each R-wave. The apex of each R-wave is then used at step 606 to determine the temporal location of the apex of each R-wave. Once the R-wave in each R-R interval has been located, the temporal location of the beginning of each Q-wave may be determined at step 608. Premature beats are removed at step 610. At step 612, the temporal location for the end of each T-wave is ~' ' The QT interval is then computed as a time difference from the beginning of the Q-wave to the end of the T-wave at a step 614.

WO 95/15116 PCT~IJS94/13736 2J7~3:~

At step 616, each R-R interval is computed. The QT intervals from step 614 and the R-R interval from step 616 may then be used at step 618 to calculate a corrected QT interval QTc for each ECG signal (electrode site) using Bazett's formula:
QT = QT inter~val c ~R R t al At step 620, the flrst measure of dispersion (Dispersionl) is computed as the maximum difference between the QT intervals taken across the N electrode sites. Similarly, at step 622, an estimate for the second measure of dispersion (Dispersion2) is computed by taking the maximum difference between the corrected QT intervals across N electrode sites. Essentially, in steps 620 and 622, the minimum QT interval is subtracted from the maximum QT interval to yield a ma~imum difference. The maximum differences for the QT
intervals and the corrected QT intervals are then used as the first and second measures of dispersion.
Figures 7A and 7B illustrate the method for computing the third and fourth measures of dispersion. Steps 702-718 are substantially identical to steps 602-618 of Figure 6. At step 720, an aYerage QT interval is computed across the N electrode sites. At step 724, a ratio is computed for each QT
interval by dividing by the average QT interval computed at step 720. An average QT ratio is then computed at step 728 by averaging the QT ratios of step 724 across the N electrode sites. Finally, at step 732, a standard deviation of the QT ratio is computed. This standard deviation is used as the third measure of dispersion (Dispersion3).
Steps 722, 726, 730, and 734 are substantially identical to steps 720, 724, 728 and 732, rc~ ,Li~CIy. However, the corrected QT intervals from step 718 are used in steps 722, 726, 730 and 734 to produce a fourth measure of dispersion (Dispersio4)based on the standard deviation of the QTc ratio.
Figure 5 illustrates the fifth method of estimating a dispersion measure.
Steps 802-806 are substantially identical to steps 602-606 of Figure 6. At step WO 9~/15116 2 1 7 7 8 ~ ~ PCrrUS94/13736 808, premature beats are removed from each EKG signal. At step 810, an average ECG waveform is computed for each R-R interval using the N
electrode sites. At step 812, the RMS (root mean square) deviation of the N
ECG signals is computed from the average ECG waveform of step 810. At step 814, the fifth measure of dispersion (Dispersion5) is taken as the ma~imum RMS deviation for each beat.
ROC curves involving any two or all three of the parameters (i.e., alternans, dispersion and heart rate variability) may be constructed to increasethe specificity of the method of the invention.
APPA~ATUS OF T~ INVENTION
The preferred ~ .o~ l of the apparatus of the invention is described with reference to Figures 8 and 9. Steps 304-308 of the method may be performed using a ( Ullv~ iUllal ECG machine or may be performed using dedicated hardware. Similarly, steps 312 and 314 may be performed on a general purpose computer or may performed by dedicated hardware.
In the preferred rll,l.o.li", ,l the invention is carried out on a heart monitoring unit (HMU) 900, shown in Figure 9A. HMU 900 includes ECG
sensing leads 901, an ECG detector and pre-processor 902 and an ECG
processing system 904. ECG detector and pre-processor 902, shown in greater detail in Figure 9B, includes a high-pass filter 9022, a pre-amplifier 9024, and a low-pass filter 9026. ECG sensing leads (i.e., electrodes) 901 provide a signal from a patient directly to high-pass filter 9022.
In an alternate ,l,u ~ ECG detector and pre-processor 902 is a ,u~ lLiul~l ECG monitoring machine.
2~ Referring now to Figure 9C, ECG processing system 904 is described.
ECG processing sysoem 904 includes a ~", ' 111;1,11 , 9040 equipped with an analog-to-digital (A/D) conversion board 90~0. The steps of the method are performed using a software program written in C
ianguage. The program follows the steps set forth above. It is WO 95/15116 ~ 3 ~ "; 9 PCT/US94113736 believed that any skilled 1,l~ " would have no difficulty writing the code necessary to perform the steps of this invention.
M . or computer platform 9040 includes a hardware unit 9041 which includes a ceMral processing unit (CPU) 9042, a random access memory (RAM) 9043, and an input/output interface 9044. RAM 9043 is also called a main memory. Computer platform 9040 also typically includes an operating system 9045. In addition, a data storage device 9046 may be included. Storage device 9046 may include an optical disk or a magnetic tape drive or disk.
Various peripheral I r ' may be connected to computer platform 9040, such as a terminal 9047, a keyboard 9048, and a printer 9049. Analog-to-digital (A/D) converter 9050 is used to sample an ECG signal. A/D
converter 9050 may also provide ~ ri~ of the ECG signal prior to sampling.
Figure 10 shows the preferred ~ ' ' of HMU 900. The system includes 16 channels to allow ~;,....Il- ,. - monitoring of a plurality of ECG
leads. High-pass filters 1004, pre-amplifiers 1006, and low-pass filters 1008 perform steps 304, 306 and 308, ~ .ly. High-pass filters 1004 have a 0.01 Hz roll-on. Low-pass filters 1008 have a 50 Hz bandwidth.
A personal computer 1010 includes an A/D converter (with pl~l~ gain), a printer 1014, a re-writable optical disk 1016, and a color monitor 1018. The program which runs on computer 1010 is preferably menu-driven. A sample menu is shown on monitor 1018.
The menu-driven program may take, as input, ;"r."" -~;.... on a patient's age, se~, medical history, and heart rate. This i"rl.",l-~;.", could then be used to select a range of standard indices (discussed below) to be used for ~ l The menu program would further allow the clinician/operator to select the A/D sampling rate, the number of ECG channels to monitor, and the gain of the A/D converter prior to c~ , data collection.
Thereafter, the clinician/operator could manually control removal of trends ~ ~ 7 7 ~ ~ ~ PCT/US94/13736 and premature beats prior to performing the dynamic analysis of alternans, heart rate Yariability, and QT interval dispersion.
Features of the menu-driven program may include selecting the method of dynamic analysis to be used and selecting the results to be displayed. For example, the clinician/operator may desire to view the ECG waveforms, the time series data (e.g., for each bin of the T-wave both before and after detrending for the alternans analysis; or for the R-R intervals in the HRV
analysis), or the actual estimate data (e.g., alternans magnitude, HRV high frequency romrnnPnt HRV low/high frequency component ratio, dispersion estimate result).
In the preferred ~ the heart monitoring unit may employ an expert system or neural network for the data analysis. An expert system will allow the monitûring unit perform complex diagnostic analyses. The program may construct ROC curves based on any two or all three of the parameters discussed above (i.e., alternans, dispersion and heart rate variability).
ANn~AL STUDY FOR ALTERNANS ANALYSIS
Animal studies were conducted by the inventors at Georgetown University School of Medicine in V,l ,, D.C. Sixteen adult mongrel dogs (20 to 30 kg) of both sexes were studied in accordance with the standards of the scientific community. The animals were pre-medicated with morphine sulfate (2 mg/kg, ~ - v ~ly) and Al- `Ih- `I'' ;I with alpha-chloralose (150 mg/kg, illL~ luu~ly), with ~ l doses of alpha-chloralose (600 mg in 60 ml saline) as required. A left Lllul.l~,uLv..l~ was performed via the fourth intercostal space.
A Doppler flow probe was placed around the left anterior descending (LAD) coronary artery and occlusions were performed using a 2-0 silk snare.
Aortic blood pressure was measured with a Gould-Statham P50 pressure transducer. The ECG was obtained using a 7 French USCI yu~llip(jlcll catheter with an inter-electrode distance of 10 mm and an electrode width of 2 mm. The catheter was positioned in the apex of the left ventricle via a WO 95~15116 PCT/I~S94/13736 2~7~J)9 carotid artery to coincide with the ischemia. This catheter placement was found to produce optimal ECG sensing.
Bipolar ECG's were obtained with the negative pole being the second electrode of the catheter and the positive pole being a ~ llc tl~,LIu~t placed 5 l ,,.. ~ .. l~ ,.. ,.. ~ly in the lower left hip region. A pigtail pressure catheter was positioned to monitor left ventricular (LV) blood pressure. The area under the LV pressure pulse of successive beats was analyzed using the technique of complex ~ No evidence of mechanital alternans was found. The tl~Llu~cld;o~l.l,ull;~, and h~ ludyl~l~ , data were . ly recorded on a Thorn EMI FM tape recorder (45 to 50 db S/N ratio, bandwidth of each channel 0 to 625 Hz). Arterial blood pH, pC02, and P02 were monitored using an IllaLlu..~ll~Liull Laboratory 1304 blood gas analyzer and were maintained within phy~;~lu~i~, ranges by adjusting ventilation parameters of the Harvard respirator.
A bilateral stellectomy was performed to interrupt ay ~ '' ' ~ neural input to the heart. This was ~ ~U.,.l.l;-h=(l by removal of the right stellate ganglion via the right second interspace and by sectioning the ~
fibers and the caudal end of the left ganglion through the left Ll~ui~uLullly.
The ansae subclavia were left intact to permit pacing of the heart at a rate of 20 150 beats per minute. Pacing was d,~.. l.li~ll.~d by delivering electricalstimuli of 1.5 to 2 mA of 5 ms duration at a frequency of lOHz to the nerves with a Grass S44 stimulator and an SIU7 stimulus isolation unit.
At the end of each ~rPrim~nr. the taped data was low-pass filtered to limit the signal bandwidth to 50 Hz. The data was tben digitized at 500 samples per second, with a Compaq 386 computer equipped with a Metrabyte DAS-20 A/D conversion board, and stored on an optical disk. The apex of each R-wave for each of the N beats was then located by finding the peak amplitudes in the digitized signal. Each beat was indexed by n from I to N.
The R-R interval was employed to sort out and remove premature beats which could introduce artifactual spikes. The period from 60 to 290 ms following the apex of each R-wave was determined to coincide with the location of the WO95/15116 2 ~ 39 PCTIUS94/13736 ~L2-T-wave. This period wa~C divided into bins 10 ms wide for each successive beat, and the area between the ECG and the isoelectric baseline was computed for each 10 ms interval. N successive beats from control through release were then sequenced into a time series for each of the 23 10-ms bins: (X(n), n =
1,2,.. N). A sixteenth order ~ .. JlLIl filter was used for both detrending and ~IPmn~ in~ to remove the large low-frequency variation in T-wave area that occurs during occlusion and to leave a cleaner signal for spectral estimation.
Detrending was performed by low-pass filtering each time series with the Butterworth filter and then subtracting the result from the original time series to achieve a high-pass filtering function. To obtain estimates of the magnitude of beat-to-beat alternation in the amplitude of each of these twenty-three time series, complex ~' (as set forth above) was used.
The effects of LAD coronary artery occlusion and reperfusion on T-wave alterrlans were tested before and after ~yll~ L~.. ,iiC .I~ V~.Liol~ and ctim~ n Baseline data was obtained for four minutes, the artery was occluded for eight minutes followed by abrupt release (reperfusion) and a 30-minute rest period. As set forth above, heart rate was maintained constant by atrial pacing at 150 bpm during assessment of the magnitude of alternans.
In eight dogs, a ~ occlusion was followed by a control occlusion with nerves intact. The occlusion-release sequence was repeated after stPllate ganglion ablation. Finally, the left stellate ganglion was stimulated two to three minutes prior to occlusion, during the second and fifth minutes of occlusion, and during reperfusion. In the second group of eight dogs, the order of v.,~lliull~ was changed to rule out sequence-related error by omitting the occlusion with nerves intact.
Figures 11A-13A show, l~,*,.,.,Liv.,'y, an cl~.~,LIu~.~ldio~ lll recorded within the left ventricle before, during, and after coronary artery occlusion ina single lC~ v~ animal. Figures 11B-13B show ~ Of six successive beats. Prior to occlusion (Flgure 11), the T-waves of each succeeding beat are uniform. After four minutes of coronary artery occlusion WO 95/15116 2 1, 7 ~ 3 3 ~ PCT/US94/13736 (Figure 12), there is marked alternation of the first half of the T-wave, coinciding with the vulnerable period of the cardiac cycle. The second half of the T-wave remains uniform. After release of the occlusion (Figure 13), alternans is b;dilcuLiullal, with T-waves alternately inscribed above and below the isoelectric line.
Coronary artery occlusion and reperfusion both resulted in significant increases in the magnitude of beat-to-beat alternation in T-wave amplitude.
Figure 14 shows a surface plot display derived by complex i~ ';ùA. of the T-wave of the Cl~,LIu~,aldiO~lalll before, during, and after coronary arteryocclusion in eight dogs with intact cardiac innervation (Figure 14A); after bilateral stellectomy in six dogs (Figure 14B); and during 30 sec of stimulationof the ansa subclavia of the rir~ i Ieft stellate ganglion in eleven dogs (Figure 14C).
The increase in alternans was evident within two to three minutes of occlusion and progressed until the occlusion was terminated at eight minutes.
Upon reperfusion, there was an abrupt increase in alternans which lasted less tban one minute. A remarkable feature is that the pattern of alternation during reperfusion was bi-directionai, with T-waves occurring alternately above and below the isoelectric line (Figure 13).
The time course of onset and offset of T-wave alternans during the ûcclusion-release sequence coincides with the ~il appearance of malignant ~a~ a lhylllll;a~ including ventricular fibrillation. Figure 15 shows a correlation between the occurrence of ~r ventricular fibrillation and T-wave alternans in ten dogs. Dogs which fibrillated exhibited a rapid rise in aiternans within the first three or four minutes of occlusion and this change was si~ll;Guall~ly more marked than that observed in animals which survived the entire occlusion-release sequence (~=p<0.001. Values are means +
S.E.M.). The results were analyzed using a one-way ANOVA with Scheffé
correction for multiple r~ In both groups, the control values did not differ ~ ll;rluall~ly from the normâl distribution by the Kolmogorov-Smirnov test.

WO95/1~116 PCT/US94/13736 2~ ~83~ ~

It is noteworthy that aiternans is marked, though short lasting, during This transient period of heightened vulnerability to fibrillation is tilought to be due to liberation of washout products of cellular ischemia. The differing, ' ~ responsible for vulnerability during occlusion and reperfusion may account for the contrasting alternation pattern in T-wave OY
The studies ~' that the ~y . ' nervous system exerts a prominent effect on T-wave alternans, a finding which is consistent with its established ~II~ J~ , influence. During coronary artery occlusion, stellectomy (Figure 14B) reduced alternans during the early phase of occlusion [from 15.8 i 6.6 at 4 minutes during control to 4.7 i 1.0 mV x ms (means i S.E.M., p<0.05)], coinciding with the time when neural activity is high in intact animals. However, later in the occlusion, extra-adrenergic factors may play a role.
Sympathetic neural influences during the reperfusion phase also appear to be tracked reliably by the present techniques. It was observed that stellate ganglion ablation increased T-wave aiternans during reperfusion Ifrom 19.8 :t 3.0 to 29.8 i 3.3 mV x ms (p<0.02)]. This concurs with a previous study indicating that stellectomy enhances reperfusion-induced vulnerability to fibrillation. Stellate ganglion stimulation restored the magnitude of alterrlansto a value which was not statistically different from pre-uk~ lv~Liull levels.
The link between alternans and vulnerability is L ' c;d by the finding that alternans coincides with the established timing of the vulnerable period in the cardiac cycle. ~u~ J of successive beats indicates that alternation is restricted to the first half of the T-wave (Figures 1 lB-13B). This remained constant in all animals studied under the changing conditions of ~ylll~ill.,.ic nervous system stimulation or ~ ,lva~iull.
ANMAL STUDY FOR HEART RATE VARL~BILITY ANALYSIS
An additional animal study conducted by the inventors was performed to verify the correlation between heart rate variability and alternans. This WO 95/15116 ~ ~ 7 ~ ~ 3 ~ PCT/US94/13736 additional study was performed substantially as set forth above. Six adult mongrel dogs were used. LAD occlusion for ten minutes was followed by abrupt release. T-wave alternans appeared within three minutes of occlusion and increased to 8.97 ~t 1.58 mVolts-msec by the fourth minute coinciding S with maximum changes in ~ala~y~ aih~t;1 (HF) activity and in the ratio of ai}~",ic to flala~ ,aill.,Lic (LF:HF) activity. This is illustrated in Figure 16, where 1602 represents ~ala~ JaL~ , activity (HF Cull r t) and 1604 represents the ratio of ~ylll~aL~ , to IJala~ JaL}I~,iiC activity (LF:HF ratio).As can be seen from inspection, ~yllll~aill~,.ic activity increases during occlusion while f/ala~ylll~aLl~ ic activiy decreases. At reperfusion, there is no change in autonomic activity.
It is important to note that these ùh~ a~iu~ concur precisely with previous studies in which nerve activity to the heart was measured using recording electrodes and vulll~.ab;lily to ventricular fibrillation was assessedby ~.~ ., ' cardiac electrical sfirnlll7finn In these C~l,.,lill.. ,llL~, it was shown that a major increase in ~ylll~,aLll.,Li~, activity ~,UIII ~ to increased y to ventricular fibrillation. See F. Lombardi, R.L. Verrier, B.
Lown, ~r~ between ~lllpaih.,Lic neural activity, coronâry dynamics, and vulnerability tû ventricular fibrillation during myocardial ischemia and IC~ AmericanHear.fJournal,vol. 105,1983,pp.958-965. Amajor advantage of the method of the invention is that i.,F~ derived in such previous invasive studies can be obtained completely from the body surface ECG by combining heart rate variability and T-wave alternans Illca~u CLINICAL APPLICAB~TY
An ECG suitable for the analysis of heart rate variability is easily measured using standard surface electrode çnnfi~ll~finnc However, alternans and dispersion require more ~ sensing techniques.
With respect to alternans, the inventors have discovered that positioning the ECG sensing erectrode into the apex of the left ventricle produces an optimal ECG signal for sensing alternans. This illLIacaviLal~ electrode WC 95/15116 2 ~ 7 7 8 ~ q PCT/US94113736 placement, however, requires invasive arld hazardous procedures such that its clinicai, diagnostic applicability is limited. What is needed is a method fo}
sensing T-wave aiternans non-invasively on the surface of the body.
Before discussing sensing of the electrical activity of the heart, it is helpful to understand a few basic principles. The electrical signals that are sensed as an ECG include electrical currents that flow through the body as a result of d ~ ;"" and lr~ ) of the myocardial cells. This electrical activity may be sensed as a voltage between areas of the body (e.g., between the chest proximate the heart and an arm or leg).
Th~t r~ fi~lly, tbe voltage "V" at a position (xp,yp,zp) due to a charge "q" at (xi,yj,z,) is given by the following equation:
V= q - V

4~e\/(Xp-X!)2+(y _y)2~(z _Z,~;)2 ECi. (24) where: e = permitivily cons~
It is assumed that V~,f is æro for a unipolar electrode, as discussed below. If the heart is modelled as a collection of charges then the equation directiy below will ~pl~ the voltage VI~ sensed by an electrode located at a point (xp,yp,zp).
E~i. (2~) ~ J ~ 4~Te~j(x -X)2 + (y _y)2 + (Z _z~)2 Under stable ~ the charges of the heart will repeat almost identically to create a stable ECG signal. That is, the charge distribution occurring x msec after the R-wave of one cardiac cycle will be nearly identical to the charge ~ictrihl.tif)n occurring x msec after the R-wave of the next cardiac cycle.
When alternans is present, however, the charge ~ ' will be modulated such that the charge distribution occurring x msec after the R-wave of successive cardiac cycles can be modeled as a static charge distribution pius W095115116 ~ ~ 7~ PCT/IJS94113736 a time varying ~ ,, the source of the alternans. This time var,ving charge .' ' resulting from alternans may be r~pre~n~rd by:
q~ "s = q cos(27r~fA~t) where: 4 = the magnitude of the alterna~ing chargeE~I- (26) fAL~r = alternahon frequency (Hz) t = O, 1, 2, . . . number of beats Locating the alternans charge at (0,0,0) produces an oscillating voltage at (xp,yp,zp) as follows:
q cos(2~fOt) Val~t~ ~ 1 2 2 2 4~ Xp +Yr +ZP
wh~re: V~ t~ = the magnitude of the alrernans voltage measured at a point (xp,y Eq. (27) This results in a total voltage at point (xp,yp,zp) of:
V~oral = Vnont + V~trn~u Eq. (28) V,~,~,, consists of an alternating component plus a constant ~ r ' To maximize the amount of alternating component detected, (xp,yp,zp) must approach (0,0,0). That is, the detecting electrode must be located as close as possible to the portion of the heart that is generating the alternation signal.
For sensing a normal ECG, limb leads, such as lead 11 (left leg with respect to right arm) can be used. Limb leads, however, are incapable of detecting the small amplitudes of alternans. T I"Lill~ ly, the inventors have discovered that alternans is a regional r~ that can be reliably detected via the precordial ECG leads.
By regional, it is meant that the alternans emanate from the injured or ischemic portion of the heart. For example, it was found that the alternation wo 95/1511~ 2 1 7 7 ~ 3 ~ PCT/US94113736 ~8-signal is strongest in the left ventricle (LV) illLlawv;kuy ECG during a left anterior descending (LAD) coronary artery occlusion. In fact, it was noted that alternation is twelve times greater as recorded from a LV i~ a~viL~ly catheter as compared with a right ventricle (RV) illLl~1~viLaly catheter.
Cu--cD~ulldill~ to this discovery, the inventors have found that alternans couldbe detected in the precordial surface ECG leads 1~ u~ lr, to the injured portion of the heart. Note that the terms "lead" and "electrode" are used ,h~ ,~bly herein.
The precordial or chest leads are unipolar electrodes which sense the ECG signal at the surface of the body. A unipolar electrode senses a positive electrical current with respect to a neutral lead. The neutral lead is an average of the voltage on the three standard limb leads: left leg, left arm, and right arm. Ideally, the voltage on the neutral lead is zero.
The location of the precordial leads on the body surface is shown in Figures 17A-17C. The precordial leads include leads Vl through V9 for the left side of the body and leads VIR through V9R for the right side of the body.
Note that lead Vl is the same as lead V2R and that lead V~ is the same as lead VIR.
The present invention is concerned primarily with precordial leads Vl through V6 because they are closest to the heart and, therefore, yield the strongest ECG signals. Figure 18 is a cross-sectional view of the human chest area 1802 taken along a horizontal axis 1702 shown in Figures 17A and 17B.
Figure 18 illustrates the position of the heart 180'L in relation to front chestwall 1806. The relative positions of precordial leads Vl through V6 and the ~ u~ . r~ normal ECG signals present at each position are also shown.
Note that lead V5 resides directly over the left ventricular surface.
The inventors have discovered that leads V5 and/or V6 are optimal for sensing alternans which result from injury to the left ventricle (e.g., obstruction of the left anterior descending artery), and leads V, and/or V2 are optimal for sensing injuries such as obstruction of the right-side coronary circulation. Additional precordial leads, such as Vg, may be useful for sensing WO 95/15116 2 ~ 7 7 ~ ~ ~ PC'r/US94Q3736 alternans resulting from remote posterio} wall injury. Thus, a physician may use the complete precordial lead system to obtain precise regarding the locus of ischemia or injury.
In order to achieve the maximum sensitivity for alternans sensing, attenuation by the skin and other body tissues must be reduced. Attenuation by the relatively large impedance provided by the skin can be overcome by proper skin abrasion, electrode jelly, or the use of needle electrodes. FurLher reduction in attenuation can be achieved by selecting the path of least resistance to the heart. This includes placing the electrodes between the ribs rather than over them.
Figures l9A-21A show continuous ECG tracings obtained ~i,...,ll- ... v -ly from lead 11, lead V" and a left ventricular illLla1av;Laly lead, ,c".~ ,ly, during LAD coronary artery occlusion in a chloralose-~nPcthPti7P~l dog. Figures 19B-ZlB show ~ of the successive beats of Figures l9A-21A, I~,s~ ,Li~.ly. Note that the oU~ illlpVo~ll waveform from lead 11 (Figure 19B) shows no ~:v..olo~ll~ly detectable alternans. Lead V5 (Figure 20B), however, shows marked alternation in the first half of the T-wave, ~ q.- l;l~ to the alternation observed in the illLl~l~,aviialy lead (Figure 21B).
20 ~ ................................... of T-wave alternation from lead 11, lead V5, and a left ventricular illLlal,aviLaly lead during LAD coronary artery occlusionin seven dogs was performed. The results are shown graphically in Figure æ
as a ~ of alternans energy from Leads 11 and V5 with reference to the LV illLIacdv;Laly lead. E~xact correlation with the illLla1avi~ly lead will produce a line with a 45~ angle. The significant linear l~,laLiu.. O~ (r2 =
0.86) between signals detected in V5 and the LV illLIacav;Laly lead indicated that the precordial lead can be used as a surrogate, obviating the need to placea catheter in the heart. The slope in V5 (0.17 :t 0.05) was s;~;-lirl~,allLly greater than in lead 11(0.08 ~ 0.02) (p<0.001). This finding is consistentwith Equation 22 with predicts a linear l~_laLiO.. olli~ between the detecting electrode and the source. As shown, the signal frvm lead V5 is clearly laFer WO 95/15116 2 ~ 7 ~ ~ 3 ~ PCTIUS94/13736 than that of lead II. The i.A~viialy lead provides a stronger signal than both lead 11 and V5.
Under certain clinical conditions, it may be ad~ ~ to record alternation from the right ventricle (RV) because of the nature of the cardiac pathology. For example, under conditions of right heart llyp. l LIU~I-Y or otherpatbology, or right coronary artery disease, the maximum expression of alternation may be detectable from a catheter positioned in the RV. Since a catheter can be positioned from the venous side of the circulation, the RV
,"~" . ,;, lil,., is relatively low risk and routine.
In humans, coronary angioplasty was performed in seven patients with greater than 70% stenosis of the LAD coronary artery. The angioplasty induced a three minute occlusion and reperfusion. Significant increases in T-wave alternans occurred within two minutes of occlusion and within ten seconds of l~ ' Alternans occurred I ' 'S/ in leads V2, V3 and V4, ~.UII~i:~Ul~lill2; to the sites overlying the ischemic zone. The alternans level was si~llir.~.-~ly greater than that observed in leads 11, V" Vsand V6 and in the Frank leads (see E. Frank, "An accurate, clinically practical system for spatial ~ Ul~ldiu~l~lly~ circr~lanon, vol. 13, 1956, pp. 737-749). Alternation invariably occurred in the first half of the T-wave as predicted above.
Figure 23 is a surface plot display obtained by the method of complex '~ ' ' (as set forth above) of the T-wave of the V4 precordial lead during ~ - U - heart rhythm in a l~ ~iiv~ patient during ~ 5/-As can be seen, within two minutes of occlusion there was a significant increase in T-wave alternans which persisted throughout the occlusion. A
marked surge in alternans upon reperfusion lasted less than one minute.
Figure 24 shows the level of T-wave alternans as a function of recording site in seven patients at three minutes of angioplasty-induced occlusion and upon balloon deflation. Alternans detected during occlusion in leads V2, V3 and V4 (the sites overlying the ischemic zone) was S;~jllir~ ily WO 95/15116 PCrr[TS94/13736 ~ 2~7~39 greater than in leads 11, Vl, V5 and V6. Dunng l ' 1. alternans levels in leads Vl-V4 were ~ lirl~ly greater than in leads 11, V5 and V6.
The precordial leads may also be used to sense a plurality of ECG
signals for the measure of dispersion. Alternatively and as a ~UIIIIJIU~ i to body mapping, a plurality of electrodes may be placed across the chest and back of a patient (e.g., 30 electrodes across the front and 30 electrodes acrosstbe back) to optimize the measure of dispersion. This electrode ~u~ r~
of illustrated in Figures 25A and 25B. Figure 25A illustrates a possible electrode ~--r~ for the chest. Figure 25B illustrates a possible electrode ~ .. ri~.,,AI;.. l~ for the back.
CONCLUSION
The ability to sense alternans non-invasively from a surface ECG via the precordial leads and to track the alternans dynamically yields a major advance in the quest for predicting SCD. Couple this with an analysis of heart rate variability to determine the relative influence of the ~ylll~G~l~,Li~, and ic nervous systems and with a measure of dispersion to improve the specificity of the alternans measure, and a diagnostic tool of ~ ,l r~r .l. ., ~ .1 value in the field of cardiology results.
The inventors ~ . ' producing several indices for tne analysis of the alternans, dispersion and heart rate varjability data. These include a T-wave alternans index, a heart rate variability index, a dispersion index and several cross-correlation indices. The T-wave alternans index (expressed in mV msec) may be norrnalized for age, gender, medical history, heart size, heart rate, etcetera. Tables of normal data for the alternans index could be established durjng exercise or behavioral stress tests. Monitored values of alternans could then be compared to this standard index to yield diagnostic i"r.." l;.... on cardiac health. This includes detecting and locating ischemic or injured portions of the heart. Because of the regional nature of alternans, c-.--~ ùll of the alternans from each precordial lead with a ~UII~

WO 95/15116 2 ~ ~ 7 ~ ~ 9 PCT/US94/13736 .

standard index value for that lead would allow an ischemic or injured site to be located without the need for invasive ~
The alternans index may be developed along the lines of arterial blood pressure indexes, for example, where pressure values in excess of 140mmHg/9OmmHg are deemed to be in the range where treatment is indicated.
The heart rate variability index may be expressed as an HF amplitude (in millicPr~m~lc) and a LF/HF ratio. Normative data may be established for both endpoints. It will be important to establish when ~y~ .L~.~,.;c activity isexcessively high and/or when ~ LDylll~lLt~ activity is low. In addition, the Very Low Frequency and Ultra Low Frequency spectral portions of heart rate variability appear to be powerful predictors of arrhythmia which may be used to provide additional diagnostic i ,r.-""-~;..., regarding myocardial infarction and SCD.
The cross-correlation index recognizes that a ~ ;.", of high degree of alternans and low heart rate variability indicates a condition is which the heart is ~ Li~,ulGlly prone to ventricular fibrillation. This is based on the fact that lowered heart rate variability indicates high DyllllJdtil~, and low ~claDylll~Jdth~ activity. It is anticipated that a " -~h. .,~ l function (e.g., a product of the alternans and heart rate variability indices, a power function,etcetera) will be developed to produce the cross-correlation index from the alternans index and the heart rate variability index. Empirical data will be required to establish the precise I....llLiL~ Li~ Li~ J between the two. The use of ROC curves will establish a result with the highest sensitivity and specificity in the prediction of sudden cardiac death.
It is .' 1 that the invention will have great utility in the d~ L of drugs, as their effects on autonomic activity and on the heart itself can be closely monitored.
It is further ~ -r~ ~ that the heart monitoring unit could be ",;, ~ i and il-uu-L~ulG-~,d into an illl~ .llL~d~l~ ~ldi~ L~ldefibrillator unit to sense alternans and heart rate variability, and then deliver drugs or 2 1 77~39 electricity to prevent or abort life-threatening rhythms or to revert cardiac arrest.
Although the invention has been described and illustrated with a certain degree of ~ Lh~u~ it is understood that those skilled irl the art will S recognize a variety of .. ' and ~ IU~ Lt~ within the spirit of the invention and the scope of the claims.

Claims (29)

-54-

1. A method of assessing cardiac vulnerability comprising the steps of:
sensing a plurality of ECG signals from a plurality of sites adjacent a heart;
analyzing an amplitude of beat-to-beat alternation in T-waves of successive R-R intervals of at least one of said ECG signals to obtain an alternans measure;
analyzing a magnitude of heart rate variability in successive R-R
intervals of at least one of said ECG signals to obtain a heart rate variabilitymeasure;
analyzing a magnitude of dispersion of repolarization in a QT interval across at least two of said plurality of ECG signals to obtain a dispersion measure; and simultaneously analyzing said alternans measure, said heart rate variability measure and said dispersion measure to assess cardiac vulnerability.

2. The method of claim 1, wherein said step of sensing a plurality of ECG signals comprises, for each ECG signal:
placing a precordial ECG lead on the surface of a subject's body proximate to the subject's heart to sense said ECG signal;
amplifying said ECG signal;
low-pass filtering said ECG signal; and sampling said ECG signal.

3. The method of claim 1, wherein said step of analyzing an amplitude of beat-to-beat alternation comprises:
selecting at least one of said plurality of ECG signals;
predicting the location in said ECG signal of a T-wave in each R-R
interval;

partinioning each T-wave in said ECG signal into a plurality of time divisions;
summing the samples in each of said time divisions of said selected ECG signal;
forming a time series for each of said time divisions, each time series including correponding sums from corresponding time divisions from successive ones of said T-waves; and performing dynamic estimation on each said time series to estimate the amplitude of beat-to-beat alternation for each said time division.

4. The method of claim 1, wherein said step of analyzing a magnitude of heart rate variability comprises:
selecting at least one of said plurality of ECG signals;
locating the peak amplitude in each R-R interval to find the apex of each R-wave in said selected ECG signal;
calculating the time between successive R-waves to determine a magnitude of each said R-R interval;
forming a time series with said magnitudes of said R-R intervals;
performing dynamic estimation on said time series to estimate a magnitude of a high frequency component of heart rate variability and to estimate a magnitude of a low frequency component of heart rate variability;
and forming a ratio of said magnitudes of said low frequency and said high frequency components of heart rate variabiliy, said ratio indicating sympathetic activity.

5. The method of claim 1, wherein said step of analyzing dispersion of repolarization comprises:
for each of said plurality of ECG signals, locating the peak amplitude in each R-R interval to find the apex of each R-wave, determining the temporal location of the beginning of each Q-wave based on the apex of each R-wave, determining the temporal location of the end of each T-wave, and calculating each QT interval as a time difference from the beginning of the Q-wave to the end of the T-wave; and estimating a measure of dispersion of repolarization of said QT
intervals across said plurality of ECG signals.

6. The method of claim 5, wherein said step of estimating a measure of dispersion comprises:
calculating a maximum difference between said QT intervals taken across said plurality of ECG signals to estimate said measure of dispersion.

7. The method of claim 5, wherein said step of estimating a measure of dispersion comprises:
calculating each R-R interval as a time difference between successive R-waves;
using each R-R interval to correct a corresponding QT interval to produce a corrected QT interval for each QT interval; and calculating a maximum difference between said corrected QT intervals taken across said plurality of ECG signals to estimate said measure of dispersion.

8. The method of claim 5, wherein said step of estimating a measure of dispersion comprises:
averaging said QT intervals to produce an average QT interval;
dividing each QT interval by said average QT interval to produce a QT
ratio for each QT interval;
averaging said QT ratios to produce an average QT ratio; and calculating a standard deviation of said QT ratio to estimate said measure of dispersion.

9. The method of claim 5, wherein said step of estimating a measure of dispersion comprises:
calculating each R-R interval as a time difference between successive R-waves;
using each R-R interval to correct a corresponding QT interval to produce a corrected QT interval for each QT interval;
averaging said corrected QT intervals to produce an average corrected QT interval;
dividing each corrected QT interval by said average corrected QT
interval to produce corrected QT ratios;
averaging said corrected QT ratios to produce an average corrected QT
ratio; and calculating a standard deviation of said corrected QT ratio to estimate said measure of dispersion.

10. The method of claim 5, wherein said step of estimating a measure of dispersion comprises:
calculating, for each R-R interval across said plurality of ECG signals, an average ECG signal;
calculating, for each R-R interval of said plurality of ECG signals, an RMS deviation using said average ECG signal; and taking an amplitude of a maximum one of said RMS deviations as said measure of dispersion.

11. The method of claim 1, further comprising the step of:
analyzing instantaneous heart rate, arterial blood pressure and instantaneous lung volume to obtain a measure of baroreceptor sensitivity, and wherein said step of simultaneously analyzing further includes analyzing said measure of baroreceptor sensitivity to assess cardiac vulnerability.

12. The method of claim 11, wherein said step of analyzing instantaneously heart rate, arterial blood pressure and instantaneous lung volume comprises:
(1) selecting at least one of said plurality of ECG signals;
(2) sensing and digitizing a blood pressure signal representing arterial blood pressure;
(3) sensing and digitizing a respiration signal representing instantaneously lung volume;
(4) computing an instantaneous heart rate for each R-R interval in said selected ECG signal; and (5) using said heart rate, said blood pressure signal and said respiration signal to determine said measure of baroreceptor sensitivity.

13. A method of predicting susceptibility to sudden cardiac death, comprising the steps of:
(a) analyzing at least one of a beat-to-beat alternation in a T-wave of an ECG of a patient's heart and dispersion of repolarization in said ECG
of the patient's heart to assess cardiac electrical stability; and (b) analyzing at least one of a magnitude of heart rate variability in said ECG of the patient's heart and baroreceptor sensitivity to assess autonomic influence on the patient's heart; and (C) performing steps (a) and (b) simultaneously to assess the patient's risk of sudden cardiac death, provided that alternation and heart ratevariability are not analyzed in combination without at least one of dispersion and baroreceptor sensitivity also being analyzed.

14. The method of claim 13, wherein said step (a) of analyzing comprises:
analyzing both beat-to-beat alternation and dispersion of repolarization to assess cardiac electrical stability.

15. The method of claim 14 wherein said step (b) of analyzing comprises:
analyzing both said magnitude of heart rate variability and said baroreceptor sensitivity to assess autonomic influence on the heart.

16. The method of claim 13, wherein said step (b) of analyzing comprises:
analyzing both said magnitude of heart rate variability and said baroreceptor sensitivity to assess autonomic influence on the heart.

17. The method of claim 14 wherein said step of analyzing beat-to-beat alternation, comprises:
(1) sensing an ECG signal from the patient's heart, said ECG signal having a plurality of R-R intervals;
(2) digitizing said ECG signal;
(3) predicting the location in said ECG signal of said T-wave in each R-R interval;
(4) partitioning each T-wave in said ECG signal into a plurality of time divisions;
(5) summing the samples in each of said time divisions of said ECG
signal;
(6) forming a time series for each of said time divisions, each time series including corresponding sums from corresponding time divisions from successive ones of said T-waves; and (7) performing dynamic estimation on each said time series to estimate the amplitude of beat-to-beat alternation for each said time division.

18. The method of claim 17 wherein said step of analyzing dispersion of repolarization comprises:
(1) sensing a plurality of ECG signals from a plurality of sites adjacent a heart, each of said plurality of ECG signals having a plurality of R-R intervals;
(2) for each of said plurality of ECG signals, i) locating the peak amplitude in each R-R interval to find the apex of each R-wave, ii) determining the temporal location of the beginning of each Q-wave based on the apex of each R-wave, iii) determining the temporal location of the end of each T-wave, and iv) calculating each QT interval as a time difference from the beginning of the Q-wave to the end of the T-wave; and (3) estimating a measure of dispersion of repolarization of said QT
intervals across said plurality of ECG signals.

19. The method of claim 18 wherein said step (b) of analyzing comprises:
analyzing both said magnitude of heart rate variability and said baroreceptor sensitivity to assess autonomic influence on the heart.

20. The method of claim 19, wherein said step of analyzing heart rate variability comprises:
(1) sensing an ECG signal from the patient's heart, said ECG signal having a plurality of R-R intervals;
(2) digitizing said ECG signal;
(3) locating the peak amplitude in each R-R interval to find the apex of each R-wave in said ECG signal;
(4) calculating the time between successive R-waves to determine a magnitude of each said R-R interval;

(5) forming a time series with said magnitudes of said R-R
intervals;
(6) performing dynamic estimation on said time series to estimate a magnitude of a high frequency component of heart rate variability and to estimate a magnitude of a low frequency component of heart rate variability;
and (7) forming a ratio of said magnitudes of said low frequency and said high frequency components of heart rate variability, said ratio indicating sympathetic activity.

21. The method of claim 20, wherein said step of analyzing baroreceptor sensitivity comprises:
(1) sensing and digitizing an ECG signal from the patient's heart, said ECG signal having a plurality of R-R intervals;
(2) sensing and digitizing a blood pressure signal representing arterial blood pressure;
(3) sensing and digitizing a respiration signal representing instantaneous lung volume;
(4) computing an instantaneous heart rate for each R-R interval in said ECG signal; and (5) using said heart rate, said blood pressure signal and said respiration signal to determine said baroreceptor sensitivity.

22. An apparatus for predicting susceptibility to sudden cardiac death, comprising:
first means for analyzing at least one of a beat-to-beat alternation in a T-wave of an ECG of a patient's heart and dispersion of repolarization in said ECG of the patient's heart to assess cardiac electrical stability; and second means for analyzing at least one of a magnitude of heart rate variability in said ECG of the patient's heart and baroreceptor sensitivity to assess autonomic influence on the patient's heart; and third means for simultaneously analyzing assessment of cardiac electrical stability from said first means and autonomic influence on the patient's heart from said second means to predict the patient's risk of sudden cardiac death, provided that alternation and heart rate variability are not analyzed in combination without at least one of dispersion and baroreceptor sensitivity also being analyzed.

23. The apparatus of claim 22, wherein said first means comprises:
fourth means for analyzing beat-to-beat alternation to assess cardiac electrical stability; and fifth means for analyzing dispersion of repolarization to assess cardiac electrical stability.

24. The apparatus of claim 23, wherein said second means comprises:
sixth means for analyzing said magnitude of heart rate variability to assess autonomic influence on the heart; and seventh means for analyzing said magnitude of heart rate variability to assess autonomic influence on the heart.

25. The apparatus of claim 24, further comprising:
means for sensing a plurality of ECG signals from a plurality of sites adjacent a heart, each of said plurality of ECG signals having a plurality of R-R intervals; and means for digitizing said plurality of ECG signals.

26. The apparatus of claim 25, wherein said fourth means comprises:
means for predicting the location in a selected ECG signal of a T-wave in each R-R interval;

means for partitioning each T-wave in said selected ECG signal into a plurality of time divisions;
means for summing the samples in each of said time divisions of said selected ECG signal;
means for forming a time series for each of said time divisions, each time series including corresponding sums from corresponding time divisions from successive ones of said T-waves; and means for dynamically estimating on each said time series to estimate the amplitude of beat-to-beat alternation for each said time division.

27. The apparatus of claim 26, wherein said fifth means comprises:
means for locating the peak amplitude in each R-R interval to find the apex of each R-wave in each of said plurality of ECG signals;
means for determining the temporal location of the beginning of each Q-wave based on the apex of each R-wave;
means for determining the temporal location of the end of each T-wave;
means for calculating each QT interval as a time difference from the beginning of the Q-wave to the end of the T-wave; and means for estimating a measure of dispersion of repolarization of said QT intervals across said plurality of ECG signals.

28. The apparatus of claim 27, wherein said sixth means comprises:
means for locating the peak amplitude in each R-R interval of a selected ECG signal to find the apex of each R-wave;
means for calculating the time between successive R-waves to determine a magnitude of each said R-R interval;
means for forming a time series with said magnitudes of said R-R
intervals;
means for performing dynamic estimation on said time series to estimate a magnitude of a high frequency component of heart rate variability and to estimate a magnitude of a low frequency component of heart rate variability; and means for forming a ratio of said magnitudes of said low frequency and said high frequency components of heart rate variability, said ratio indicating sympathetic activity.

29. The apparatus of claim 28, wherein said seventh means comprises:
sensing and digitizing a blood pressure signal representing arterial blood pressure;
sensing and digitizing a respiration signal representing instantaneous lung volume;
computing an instantaneous heart rate for each R-R interval in a selected ECG signal; and using said heart rate, said blood pressure signal and said respiration signal to determine said baroreceptor sensitivity.