CN103549945B - Method for recognizing pulse rate and blood oxygen saturation degree through cardiac contraction process characteristic - Google Patents

Abstract

The invention discloses a method for recognizing pulse rate and blood oxygen saturation degree through cardiac contraction process characteristic. The method comprises the following steps: (1), continuously acquiring red light electric signals detected by a blood oxygen detection probe to obtain a waveform signal sequence {an}; (2) differentiating the waveform signal sequence {an} to obtain a differential signal sequence {bn}; (3) turning the signal waveform of the differential signal sequence {bn} upside down, and removing a negative part to obtain a characteristic signal sequence {cn}; (4) finding out a convex part in the signal waveform of the characteristic signal sequence {cn} and extracting the characteristic thereof; (5) extracting the convex part caused by a cardiac contraction process; (6) calculating the pulse rate according to the characteristic of the convex part caused by the cardiac contraction process; (7) processing infrared electric signals according to the steps (1) to (5) to obtain the convex part caused by the cardiac contraction process in the infrared electric signals, and calculating the blood oxygen saturation degree by combining with the characteristic of the convex part caused by the cardiac contraction process in the red light electric signals.

Description

By the method for systole feature identification pulse frequency and blood oxygen saturation

Technical field

The present invention relates to medical detection field, particularly relate to the method by systole feature identification pulse frequency and blood oxygen saturation.

Background technology

Along with the development of modern medical service technology and related discipline, medical monitoring instrument has become the large quasi-instrument of medical electronic apparatus indispensable, plays a part more and more important within the hospital.The use of monitor, not only alleviates the work of medical worker, improves the efficiency of nursery work, the more important thing is and makes doctor can understand the state of an illness at any time, can process in time, improve nursing quality when there is emergency situation.In the physiological parameter of monitoring, except electrocardio, blood pressure etc., the oxygen concentration in blood of human body and blood oxygen saturation and being determined at of pulse frequency are also of great significance clinically.In the monitoring of surgical operation or critical patient, avoid patient's anoxia, it is very necessary for understanding oxygen content in blood in time.

Blood oxygen detecting probe is used to detect the index such as pulse frequency, blood oxygen saturation, generally includes HONGGUANG, infrared transmitter and receptor.When finger clamp is inside blood oxygen detecting probe, HONGGUANG, infrared transmitter be emission of light respectively, light therethrough is pointed and is transmitted into receiver end, and light signal strength is converted to electrical signal intensity (voltage) by receptor, then gathers this voltage by mould data converter.

Fig. 1 is that under different blood oxygen saturation, blood is to the absorbance curves of HONGGUANG and infrared light, and vertical coordinate is that under different blood oxygen saturation, blood is to the absorbance of HONGGUANG and infrared light, and abscissa is wavelength.In figure, HHb is deoxyhemoglobin, and expression blood oxygen saturation is 0%, O ₂hb is band oxygen hemoglobin, and represent that blood oxygen saturation is 100%, dotted line is 50% blood oxygen saturation absorbance curves, can find out the light for Same Wavelength in figure, and under different blood oxygen saturations, blood is different for the absorbance of light.When heart contraction, blood can be filled into the peripheral vessel of finger, and because blood is now many, light can be absorbed often, and the voltage therefore collected can be lower; During diastole, blood meeting reflux veins, now the blood of finger is fewer, and light is absorbed relatively less, and the voltage therefore collected can grow.As shown in Figure 2, be the electrical signal intensity fluctuation situation of HONGGUANG.Can find out, the signal waveform in figure is periodic, and wherein the frequency of waveform is exactly pulse frequency (number of times that pulse is per minute).If find out crest or the trough of signal waveform, the interval calculated between crest or between trough can calculate the cycle.

Prior art adopts base-line method to look for crest, trough.Baseline can be waveform average, and also can be the horizontal line that other method is arranged, as shown in Figure 3, wherein horizontal line be baseline, and crossing baseline, upwards to go to peak be crest, and crossing baseline, to go to minimum point be downwards trough.But because the motion of patient's hands or probe to loosen etc. reason, cause waveform entirety up or walk downward, this phenomenon is called as baseline drift, and waveform entirety is upwards walked as shown in Figure 4.Can find out, when baseline drift, the False Rate of base-line method identification crest, trough is very high, thus cause the pulse frequency that calculates and blood oxygen saturation also inaccurate.

Summary of the invention

Embodiment of the present invention technical problem to be solved is, provides a kind of method by systole feature identification pulse frequency and blood oxygen saturation.Its object is to by identifying the feature produced by heart contraction in signal waveform, thus determine each cycle exactly, realize the calculating of pulse frequency and blood oxygen saturation.

In order to solve the problems of the technologies described above, embodiments provide a kind of method by systole feature identification pulse frequency and blood oxygen saturation, comprising step:

(1) the HONGGUANG signal of telecommunication transformed by HONGGUANG optical signal that detected by blood oxygen detecting probe of continuous collecting, and carry out filtering, obtain waveshape signal sequence { a _n;

(2) to described filtered waveshape signal sequence { a _ncarry out difference processing, obtain differential signal sequence { b _n, and b _n=a _n+1-a _n;

(3) by described differential signal sequence { b _nsignal waveform spin upside down, and remove negative fraction waveform, obtain characteristic signal sequence { c _n, wherein c _n=-b _n(b _n≤ 0), c _n=0 (b _n> 0);

(4) described characteristic signal sequence { c is found out _nsignal waveform in whole bossings, and extract the feature of described whole bossing, comprise the origin sequences { s of bossing _n, terminal sequence { e _n, wide sequence { w _nand amplitude sequence { h _n;

(5) according to the width w of each described bossing _nwith amplitude h _n, judge whether each described bossing is caused by systole, and extract the bossing caused by systole;

(6) according to the feature calculation pulse frequency of the described bossing caused by systole;

(7) the infrared electro signal transformed by infrared light optical signal that blood oxygen detecting probe detects is processed to (5) according to described step (1), to obtain the bossing caused by systole in infrared electro signal, and in conjunction with the bossing caused by systole in described infrared electro signal characteristic sum described in the feature calculation blood oxygen saturation of bossing that caused by systole in the HONGGUANG signal of telecommunication.

Further, described step (4) specifically comprises step:

(4-1) for described characteristic signal sequence { c _n, find from the left side, think characteristic signal sequence { c _nthe starting point that the point being greater than 0 is a bossing is become from 0, think from being greater than the terminal that 0 point becoming 0 is current bossing, thus extract the origin sequences { s of whole bossing _nand terminal sequence { e _n;

(4-2) wide sequence { w of described whole bossing is extracted _n, wherein w _n=e _n-s _n;

(4-3) amplitude sequence { h of described whole bossing is extracted _n, wherein, h _n=max (Y (s _n) ~ Y (e _n)), namely get the maximum of bossing as amplitude.

Further, described step (5) specifically comprises step:

(5-1) set a classification boundaries, and described classification boundaries is a circle, the center of circle is (w _c, h _c), radius is r;

(5-2) distance of each bossing to described classification boundaries is calculated, if distance is greater than 0, then thinks that bossing is not caused by systole, otherwise think that bossing is caused by systole, wherein, bossing to the distance of described classification boundaries is

(5-3) bossing caused by systole is judged as in extraction step (5-2).

Wherein, the classification boundaries of described setting is obtained by following steps:

(5-1-1) electrical signal data detected by blood oxygen detecting probe of a large amount of different people is gathered;

(5-1-2) described electrical signal data is processed to (4) according to described step (1);

(5-1-3) with sequence { s' _n, { e' _n, { w' _n, { h' _nrepresent starting point, terminal, width, the amplitude sequence of bossing that are caused by heart contraction, sequence corresponding non-cardiac shrinks the bossing caused, and be X-axis with width in coordinate system, be that Y-axis is by described sequence { w' with amplitude _n, { h' _nand show;

(5-1-4) all width of the bossing caused by heart contraction and the average of the amplitude center of circle (w as described classification boundaries is calculated _c, h _c), that is, $w_c=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{w}_i^{\′},h_c=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{h}_i^{\′};$

(5-1-5) bossing that caused by heart contraction is calculated to the described center of circle (w _c, h _c) distance D'c _n, and non-cardiac shrinks the bossing that causes to the described center of circle (w _c, h _c) distance and find out distance D'c _nthe point P of maximum correspondence _in(w', h') | _{max (D'cn)}and distance the point of minimum correspondence the mid point of 2 so found out and the distance in the center of circle are the radius r of described classification boundaries, wherein,

$D^{\′}{c}_n=\sqrt{{(w_n^{\′}-w_c)}^2+{(h_n^{\′}-h_c)}^2},$

${\tilde{D}c}_n=\sqrt{{({\tilde{w}}_n-w_c)}^2+{({\tilde{h}}_n-h_c)}^2},$

$r=\sqrt{{(\frac{w^{\′}+\tilde{w}}{2}-w_c)}^2+{(\frac{h^{\′}+\tilde{h}}{2}-h_c)}^2}.$

Further, described step (6) specifically comprises step:

(6-1) origin sequences { s of the bossing caused according to described systole _n' or terminal sequence { e' _ncomputing cycle T, wherein, $T=\frac{1}{n-1}\·\underset{i=2}{\overset{n}{\mathrm{\Σ}}}{s}_i^{\′}-s_{i-1}^{\′}$ Or $T=\frac{1}{n-1}\·\underset{i=2}{\overset{n}{\mathrm{\Σ}}}{e}_i^{\′}-e_{i-1}^{\′};$

(6-2) pulse frequency is calculated according to computing cycle T, wherein, pulse frequency

Further, described step (7) specifically comprises step:

(7-1) the infrared electro signal transformed by infrared light optical signal that blood oxygen detecting probe detects is processed to (5) according to described step (1), obtain the bossing caused by systole in infrared electro signal;

(7-2) using the starting point of bossing that caused by systole respectively in infrared electro signal and the HONGGUANG signal of telecommunication as crest, terminal is as trough, and wherein, crest, the trough range value sequence of the HONGGUANG signal of telecommunication are designated as { Rp respectively _n, { Rv _n, crest, the trough range value sequence of infrared electro signal are designated as { IRp respectively _n, { IRv _n;

(7-3) HONGGUANG interchange value R is calculated _aC, infrared light interchange value IR _aC, HONGGUANG D. C. value R _dC, infrared light D. C. value IR _dCwith R value, wherein,

$R_{\mathrm{AC}}=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{Rp}}_i-\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{Rv}}_i,$

$I{R}_{\mathrm{AC}}=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}I{\mathrm{Rp}}_i-\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}I{\mathrm{Rv}}_i,$

$R_{\mathrm{DC}}=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{Rv}}_i,$

${\mathrm{IR}}_{\mathrm{DC}}=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}I{\mathrm{Rv}}_i,$

$R=\left(\frac{R_{\mathrm{AC}}}{R_{\mathrm{DC}}}\right)/\left(\frac{{\mathrm{IR}}_{\mathrm{AC}}}{{\mathrm{IR}}_{\mathrm{DC}}}\right);$

(7-4) search according to the R value calculated the R value/blood oxygen saturation mapping table established, obtain blood oxygen saturation.

Wherein, the process of establishing of described R value/blood oxygen saturation mapping table is:

(7-4-1) collecting sample data, described sample data comprises the signal of telecommunication and corresponding blood oxygen saturation that blood oxygen detecting probe detects;

(7-4-2) signal of telecommunication of described sample data is processed to (7) according to described step (1), to obtain the R value of sample data;

(7-4-3) all R values corresponding for blood oxygen saturation same in sample data are averaged, then blood oxygen saturation and R value average one_to_one corresponding, and adopt linear interpolation to supplement blood oxygen saturation and R value makes blood oxygen saturation spacing be 1%, thus obtain described R value/blood oxygen saturation mapping table.

Above inventive embodiments is by identifying the feature produced by heart contraction in signal waveform, thus determine each cycle exactly, realize the accurate identification of pulse frequency and blood oxygen saturation, even if under the hands movement or probe of measuring people to loosen etc. the signal waveform baseline drift situation that causes of reason, also can accurately identify pulse frequency and blood oxygen saturation.

Accompanying drawing explanation

In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.

Fig. 1 be under different blood oxygen saturation blood to the absorbance curves figure of HONGGUANG and infrared light;

Fig. 2 is HONGGUANG electric signal waveform schematic diagram;

Fig. 3 is base-line method identification Wave crest and wave trough schematic diagram;

Signal waveform schematic diagram when Fig. 4 is baseline drift;

Fig. 5 is the schematic flow sheet of the method by systole feature identification pulse frequency and blood oxygen saturation that the embodiment of the present invention provides;

Fig. 6 is waveshape signal and differential signal waveform schematic diagram;

Fig. 7 is the waveform variation diagram in the one-period that caused by systole;

Fig. 8 is characteristic signal and waveshape signal waveform schematic diagram;

Fig. 9 is the idiographic flow schematic diagram of step S4 in Fig. 5;

Figure 10 is the idiographic flow schematic diagram of step S5 in Fig. 5;

Figure 11 is the schematic flow sheet of the step setting classification boundaries in Figure 10;

Figure 12 is the coordinates table diagram of the feature of bossing;

Figure 13 is the idiographic flow schematic diagram of step S6 in Fig. 5;

Figure 14 is the idiographic flow schematic diagram of step S7 in Fig. 5;

Figure 15 is R value/blood oxygen saturation mapping table process of establishing schematic diagram.

Detailed description of the invention

Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.

Embodiments provide a kind of method by systole feature identification pulse frequency and blood oxygen saturation, as shown in Figure 5, comprise step:

S1: the HONGGUANG signal of telecommunication transformed by HONGGUANG optical signal that continuous collecting is detected by blood oxygen detecting probe, and carry out filtering, obtain waveshape signal sequence { a _n;

S2: to described filtered waveshape signal sequence { a _ncarry out difference processing, obtain differential signal sequence { b _n, and b _n=a _n+1-a _n;

Waveshape signal sequence { a _nand differential signal sequence { b _nwaveform as shown in Figure 6, wherein systole correspond to waveshape signal sequence { a _nin the part of precipitous decline in each cycle, part as shown in Fig. 7 ellipse.Come as can be seen from Figure 6, in systole, differential signal sequence { b _nit is negative.In fact systole has two key characters, and one is the time span of contraction and the dynamics of contraction, represents at waveshape signal sequence { a _nwaveform on be exactly the width on abrupt slope and the slope on abrupt slope, namely differential signal sequence { b _non width and amplitude;

S3: by described differential signal sequence { b _nsignal waveform spin upside down, and remove negative fraction waveform, obtain characteristic signal sequence { c _n, wherein c _n=-b _n(b _n≤ 0), c _n=0 (b _n> 0);

Characteristic signal sequence { c _nas shown in Figure 8;

S4: find out described characteristic signal sequence { c _nsignal waveform in whole bossings, and extract the feature of described whole bossing, comprise the origin sequences { s of bossing _n, terminal sequence { e _n, wide sequence { w _nand amplitude sequence { h _n;

S5: according to the width w of each described bossing _nwith amplitude h _n, judge whether each described bossing is caused by systole, and extract the bossing caused by systole;

S6: according to the feature calculation pulse frequency of the described bossing caused by systole;

S7: the infrared electro signal transformed by infrared light optical signal that blood oxygen detecting probe detects is processed according to described step S1 to S5, to obtain the bossing caused by systole in infrared electro signal, and in conjunction with the bossing caused by systole in described infrared electro signal characteristic sum described in the feature calculation blood oxygen saturation of bossing that caused by systole in the HONGGUANG signal of telecommunication.

Further, as shown in Figure 9, described step S4 specifically comprises step:

S41: for described characteristic signal sequence { c _n, find from the left side, think characteristic signal sequence { c _nthe starting point that the point being greater than 0 is a bossing is become from 0, think from being greater than the terminal that 0 point becoming 0 is current bossing, thus extract the origin sequences { s of whole bossing _nand terminal sequence { e _n;

S42: the wide sequence { w extracting described whole bossing _n, wherein w _n=e _n-s _n;

S43: the amplitude sequence { h extracting described whole bossing _n, wherein, h _n=max (Y (s _n) ~ Y (e _n)), namely get the maximum of bossing as amplitude.

Further, as shown in Figure 10, described step S5 specifically comprises step:

S51: set a classification boundaries, and described classification boundaries is a circle, the center of circle is (w _c, h _c), radius is r;

S52: calculate the distance of each bossing to described classification boundaries, if distance is greater than 0, then thinks that bossing is not caused by systole, otherwise think that bossing is caused by systole, wherein, bossing to the distance of described classification boundaries is

S53: be judged as the bossing caused by systole in extraction step S52.

Wherein, as shown in figure 11, the classification boundaries of described setting is obtained by following steps:

S511: the electrical signal data detected by blood oxygen detecting probe gathering a large amount of different people;

S512: described electrical signal data is processed according to described step S1 to S4;

S513: with sequence { s' _n, { e' _n, { w' _n, { h' _nrepresent starting point, terminal, width, the amplitude sequence of bossing that are caused by heart contraction, sequence corresponding non-cardiac shrinks the bossing caused, and be X-axis with width in coordinate system, be that Y-axis is by described sequence { w' with amplitude _n, { h' _nand show;

Specifically as shown in figure 12, the corresponding bossing caused by heart contraction of point set shown in circle;

S514: calculate all width of the bossing caused by heart contraction and the average of the amplitude center of circle (w as described classification boundaries _c, h _c), that is, $w_c=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{w}_i^{\′},h_c=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{h}_i^{\′};$

S515: calculate the bossing that caused by heart contraction to the described center of circle (w _c, h _c) distance D'c _n, and non-cardiac shrinks the bossing that causes to the described center of circle (w _c, h _c) distance and find out distance D'c _nthe point of maximum correspondence and distance the point of minimum correspondence the mid point of 2 so found out and the distance in the center of circle are the radius r of described classification boundaries;

Wherein,

$D^{\′}{c}_n=\sqrt{{(w_n^{\′}-w_c)}^2+{(h_n^{\′}-h_c)}^2},$

${\tilde{D}c}_n=\sqrt{{({\tilde{w}}_n-w_c)}^2+{({\tilde{h}}_n-h_c)}^2},$

$r=\sqrt{{(\frac{w^{\′}+\tilde{w}}{2}-w_c)}^2+{(\frac{h^{\′}+\tilde{h}}{2}-h_c)}^2}.$

Further, as shown in figure 13, described step S6 specifically comprises step:

S61: according to the origin sequences { s of the bossing that described systole causes _n' or terminal sequence { e' _ncomputing cycle T, wherein, $T=\frac{1}{n-1}\·\underset{i=2}{\overset{n}{\mathrm{\Σ}}}{s}_i^{\′}-s_{i-1}^{\′}$ Or $T=\frac{1}{n-1}\·\underset{i=2}{\overset{n}{\mathrm{\Σ}}}{e}_i^{\′}-e_{i-1}^{\′};$

S62: calculate pulse frequency according to computing cycle T, wherein, pulse frequency

Further, as shown in figure 14, described step S7 specifically comprises step:

S71: the infrared electro signal transformed by infrared light optical signal that blood oxygen detecting probe detects is processed according to described step S1 to S5, obtains the bossing caused by systole in infrared electro signal;

S72: using the starting point of bossing that caused by systole respectively in infrared electro signal and the HONGGUANG signal of telecommunication as crest, terminal is as trough, and wherein, crest, the trough range value sequence of the HONGGUANG signal of telecommunication are designated as { Rp respectively _n, { Rv _n, crest, the trough range value sequence of infrared electro signal are designated as { IRp respectively _n, { IRv _n;

S73: calculate HONGGUANG interchange value R _aC, infrared light interchange value IR _aC, HONGGUANG D. C. value R _dC, infrared light D. C. value IR _dCwith R value;

Wherein,

$R_{\mathrm{AC}}=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{Rp}}_i-\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{Rv}}_i,$

$I{R}_{\mathrm{AC}}=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}I{\mathrm{Rp}}_i-\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}I{\mathrm{Rv}}_i,$

$R_{\mathrm{DC}}=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{Rv}}_i,$

${\mathrm{IR}}_{\mathrm{DC}}=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}I{\mathrm{Rv}}_i,$

$R=\left(\frac{R_{\mathrm{AC}}}{R_{\mathrm{DC}}}\right)/\left(\frac{{\mathrm{IR}}_{\mathrm{AC}}}{{\mathrm{IR}}_{\mathrm{DC}}}\right);$

S74: search the R value/blood oxygen saturation mapping table established according to the R value calculated, obtain blood oxygen saturation.

Wherein, as shown in figure 15, the process of establishing of described R value/blood oxygen saturation mapping table is:

S741: collecting sample data, described sample data comprises the signal of telecommunication and corresponding blood oxygen saturation that blood oxygen detecting probe detects;

S742: process according to described step S1 to S7 the signal of telecommunication of described sample data, to obtain the R value of sample data;

S743: all R values corresponding for blood oxygen saturation same in sample data are averaged, then blood oxygen saturation and R value average one_to_one corresponding, and adopt linear interpolation to supplement blood oxygen saturation and R value makes blood oxygen saturation spacing be 1%, thus obtain described R value/blood oxygen saturation mapping table.

Above inventive embodiments is by identifying the feature produced by heart contraction in signal waveform, thus determine each cycle exactly, realize the accurate identification of pulse frequency and blood oxygen saturation, even if under the hands movement or probe of measuring people to loosen etc. the signal waveform baseline drift situation that causes of reason, also can accurately identify pulse frequency and blood oxygen saturation.

Above disclosedly be only a kind of preferred embodiment of the present invention, certainly can not limit the interest field of the present invention with this, therefore according to the equivalent variations that the claims in the present invention are done, still belong to the scope that the present invention is contained.

Claims (6)

1. pass through a method for systole feature identification pulse frequency and blood oxygen saturation, it is characterized in that, comprise step:

(1) the HONGGUANG signal of telecommunication transformed by HONGGUANG optical signal that detected by blood oxygen detecting probe of continuous collecting, and carry out filtering, obtain waveshape signal sequence { a _n;

(2) to described filtered waveshape signal sequence { a _ncarry out difference processing, obtain differential signal sequence { b _n, and b _n=a _n+1-a _n;

(3) by described differential signal sequence { b _nsignal waveform spin upside down, and remove negative fraction waveform, obtain characteristic signal sequence { c _n, wherein c _n=-b _n(b _n≤ 0), c _n=0 (b _n> 0);

(4) described characteristic signal sequence { c is found out _nsignal waveform in whole bossings, and extract the feature of described whole bossing, comprise the origin sequences { s of bossing _n, terminal sequence { e _n, wide sequence { w _nand amplitude sequence { h _n;

(5) according to the width w of each described bossing _nwith amplitude h _n, judge whether each described bossing is caused by systole, and extract the bossing caused by systole;

(6) according to the feature calculation pulse frequency of the described bossing caused by systole;

(7) the infrared electro signal transformed by infrared light optical signal that blood oxygen detecting probe detects is processed to (5) according to described step (1), to obtain the bossing caused by systole in infrared electro signal, and in conjunction with the bossing caused by systole in described infrared electro signal characteristic sum described in the feature calculation blood oxygen saturation of bossing that caused by systole in the HONGGUANG signal of telecommunication;

Described step (5) specifically comprises step:

(5-1) set a classification boundaries, and described classification boundaries is a circle, the center of circle is (w _c, h _c), radius is r;

(5-2) distance of each bossing to described classification boundaries is calculated, if distance is greater than 0, then thinks that bossing is not caused by systole, otherwise think that bossing is caused by systole, wherein, bossing to the distance of described classification boundaries is

(5-3) bossing caused by systole is judged as in extraction step (5-2).

2. the method passing through systole feature identification pulse frequency and blood oxygen saturation as claimed in claim 1, it is characterized in that, described step (4) specifically comprises step:

(4-1) for described characteristic signal sequence { c _n, find from the left side, think characteristic signal sequence { c _nthe starting point that the point being greater than 0 is a bossing is become from 0, think from being greater than the terminal that 0 point becoming 0 is current bossing, thus extract the origin sequences { s of whole bossing _nand terminal sequence { e _n;

(4-2) wide sequence { w of described whole bossing is extracted _n, wherein w _n=e _n-s _n;

(4-3) amplitude sequence { h of described whole bossing is extracted _n, wherein, h _n=max (Y (s _n) ~ Y (e _n)), namely get the maximum of bossing as amplitude.

3. the method passing through systole feature identification pulse frequency and blood oxygen saturation as claimed in claim 1, it is characterized in that, the classification boundaries of described setting is obtained by following steps:

(5-1-1) electrical signal data detected by blood oxygen detecting probe of a large amount of different people is gathered;

(5-1-2) described electrical signal data is processed to (4) according to described step (1);

(5-1-3) with sequence { s' _n, { e' _n, { w' _n, { h' _nrepresent starting point, terminal, width, the amplitude sequence of bossing that are caused by heart contraction, sequence corresponding non-cardiac shrinks the bossing caused, and be X-axis with width in coordinate system, be that Y-axis is by described sequence { w' with amplitude _n, { h' _nand show;

(5-1-4) all width of the bossing caused by heart contraction and the average of the amplitude center of circle (w as described classification boundaries is calculated _c, h _c), that is, $w_c=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{w}_i^{\′},h_c=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{h}_i^{\′};$

(5-1-5) bossing that caused by heart contraction is calculated to the described center of circle (w _c, h _c) distance D'c _n, and non-cardiac shrinks the bossing that causes to the described center of circle (w _c, h _c) distance and find out distance D'c _nthe point of maximum correspondence and distance the point of minimum correspondence the mid point of 2 so found out and the distance in the center of circle are the radius r of described classification boundaries, wherein,

$D^{\′}{c}_n=\sqrt{{(w_n^{\′}-w_c)}^2+{(h_n^{\′}-h_c)}^2},$

$\tilde{D}{c}_n=\sqrt{{({\tilde{w}}_n-w_c)}^2+{({\tilde{h}}_n-h_c)}^2},$

$r=\sqrt{{(\frac{w^{\′}+\tilde{w}}{2}-w_c)}^2+{(\frac{h^{\′}+\tilde{h}}{2}-h_c)}^2}.$

4. the method passing through systole feature identification pulse frequency and blood oxygen saturation as claimed in claim 1, it is characterized in that, described step (6) specifically comprises step:

(6-1) origin sequences { s of the bossing caused according to described systole _n' or terminal sequence { e' _ncomputing cycle T, wherein, $T=\frac{1}{n-1}\·\underset{i=2}{\overset{n}{\mathrm{\Σ}}}{s}_i^{\′}-s_{i-1}^{\′}$ Or $T=\frac{1}{n-1}\·\underset{i=2}{\overset{n}{\mathrm{\Σ}}}{e}_i^{\′}-e_{i-1}^{\′};$

(6-2) pulse frequency is calculated according to computing cycle T, wherein, pulse frequency

5. the method passing through systole feature identification pulse frequency and blood oxygen saturation as claimed in claim 1, it is characterized in that, described step (7) specifically comprises step:

(7-1) the infrared electro signal transformed by infrared light optical signal that blood oxygen detecting probe detects is processed to (5) according to described step (1), obtain the bossing caused by systole in infrared electro signal;

(7-2) using the starting point of bossing that caused by systole respectively in infrared electro signal and the HONGGUANG signal of telecommunication as crest, terminal is as trough, and wherein, crest, the trough range value sequence of the HONGGUANG signal of telecommunication are designated as { Rp respectively _n, { Rv _n, crest, the trough range value sequence of infrared electro signal are designated as { IRp respectively _n, { IRv _n;

(7-3) HONGGUANG interchange value R is calculated _aC, infrared light interchange value IR _aC, HONGGUANG D. C. value R _dC, infrared light D. C. value IR _dCwith R value, wherein,

$R_{\mathrm{AC}}=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{Rp}}_i-\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{Rv}}_i,$

$I{R}_{\mathrm{AC}}=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}I{\mathrm{Rp}}_i-\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}I{\mathrm{Rv}}_i,$

$R_{\mathrm{DC}}=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{Rv}}_i,$

$I{R}_{\mathrm{DC}}=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}I{\mathrm{Rv}}_i,$

$R=\left(\frac{R_{\mathrm{AC}}}{R_{\mathrm{DC}}}\right)/\left(\frac{{\mathrm{IR}}_{\mathrm{AC}}}{{\mathrm{IR}}_{\mathrm{DC}}}\right);$

(7-4) search according to the R value calculated the R value/blood oxygen saturation mapping table established, obtain blood oxygen saturation.

6. the method passing through systole feature identification pulse frequency and blood oxygen saturation as claimed in claim 5, it is characterized in that, the process of establishing of described R value/blood oxygen saturation mapping table is:

(7-4-1) collecting sample data, described sample data comprises the signal of telecommunication and corresponding blood oxygen saturation that blood oxygen detecting probe detects;

(7-4-2) signal of telecommunication of described sample data is processed to (7) according to described step (1), to obtain the R value of sample data;

(7-4-3) all R values corresponding for blood oxygen saturation same in sample data are averaged, then blood oxygen saturation and R value average one_to_one corresponding, and adopt linear interpolation to supplement blood oxygen saturation and R value makes blood oxygen saturation spacing be 1%, thus obtain described R value/blood oxygen saturation mapping table.