CN103549945A - 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 heart contraction process feature identification pulse frequency and blood oxygen saturation

Technical field

The present invention relates to medical detection field, relate in particular to by the method for heart contraction process 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 become in the large quasi-instrument ， of a medical electronic apparatus indispensable hospital, play a part more and more important.The use of monitor, has not only alleviated medical worker's work, has improved the efficiency of nursery work, the more important thing is and makes doctor understand at any time the state of an illness, when there is emergency situation, can process in time, has improved nursing quality.In the physiological parameter of monitoring, except electrocardio, blood pressure etc., the oxygen concentration in blood of human body is that being determined at clinically of blood oxygen saturation and pulse frequency is also of great significance.In surgical operation or critical patient's monitoring, avoid patient's anoxia, it is very necessary understanding in time oxygen content in blood.

Blood oxygen detecting probe is for detecting the indexs such as pulse frequency, blood oxygen saturation, generally including HONGGUANG, infrared transmitter and receptor.When banjo splint is inside blood oxygen detecting probe, HONGGUANG, infrared transmitter be emission of light respectively, light sees through finger to being transmitted into receiver end, and receptor converts light signal strength to electrical signal intensity (voltage), then gathers this voltage by mould data converter.

Fig. 1 is the absorbance curves of blood to HONGGUANG and infrared light under different blood oxygen saturation, and vertical coordinate is the absorbance of blood to HONGGUANG and infrared light under different blood oxygen saturations, and abscissa is wavelength.In figure, HHb is deoxyhemoglobin, and expression blood oxygen saturation is 0%, O ₂hb is band oxygen hemoglobin, represents that blood oxygen saturation is 100%, and dotted line is 50% blood oxygen saturation absorbance curves, in figure, can find out the light for Same Wavelength, 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 collecting can be lower; During diastole, blood meeting reflux veins, now the blood of finger is fewer, and light is absorbed relatively lessly, and the voltage therefore collecting 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 that periodically 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, calculate between crest or the interval between trough can calculate the cycle.

Prior art adopts base-line method to look for crest, trough.Baseline can be waveform average, can be also the horizontal line that other method arranges, and as shown in Figure 3, wherein horizontal line is baseline, and crossing baseline, upwards to go to peak be crest, and crossing baseline, to go to minimum point be downwards trough.But due to the motion of patient's hands or loosening etc. the reason of probe, cause waveform integral body up or walk downward, this phenomenon is called as baseline drift, and waveform is exactly that integral body makes progress away as shown in Figure 4.Can find out, in baseline drift, the False Rate of base-line method identification crest, trough is very high, thereby causes the pulse frequency and the blood oxygen saturation that calculate also inaccurate.

Summary of the invention

Embodiment of the present invention technical problem to be solved is, provides a kind of by the method for heart contraction process feature identification pulse frequency and blood oxygen saturation.Its object is the feature being produced by heart contraction in signal waveform by identifying, thereby determines exactly each cycle, realizes the calculating of pulse frequency and blood oxygen saturation.

In order to solve the problems of the technologies described above, it is a kind of by the method for heart contraction process feature identification pulse frequency and blood oxygen saturation that the embodiment of the present invention provides, and comprises step:

(1) the HONGGUANG signal of telecommunication being 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;

(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 portion waveshape, obtain characteristic signal sequence { c _n, c wherein _n=-b _n(b _n≤ 0), c _n=0 (b _n> 0);

(4) find out described characteristic signal sequence { c _nsignal waveform in whole bossings, and extract the feature of described whole bossings, what comprise bossing plays point sequence { s _n, terminal sequence { e _n, width sequence { w _nand amplitude sequence { h _n;

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

(6) according to the feature calculation pulse frequency of the described bossing being caused by heart contraction process;

(7) the infrared electro signal being transformed by infrared light optical signal blood oxygen detecting probe being detected is processed to (5) according to described step (1), with the bossing that obtains being caused by heart contraction process in infrared electro signal, and in conjunction with the feature calculation blood oxygen saturation of the bossing being caused by heart contraction process in the feature of the bossing being caused by heart contraction process in described infrared electro signal and the described HONGGUANG signal of telecommunication.

Further, described step (4) specifically comprises step:

(4-1) for described characteristic signal sequence { c _n, from the left side, start to find, think characteristic signal sequence { c _nfrom 0, become that to be greater than 0 point be the starting point of a bossing, think and become from being greater than 0 the terminal that 0 point is current bossing, thus extract whole bossings play point sequence { s _nand terminal sequence { e _n;

(4-2) extract the width sequence { w of described whole bossings _n, w wherein _n=e _n-s _n;

(4-3) extract the amplitude sequence { h of described whole bossings _n, wherein, h _n=max (Y (s _n)～Y (e _n)), 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) calculate each bossing to the distance of described classification boundaries, if distance is greater than 0, thinks that bossing is not caused by heart contraction process, otherwise think that bossing is caused by heart contraction process, wherein, bossing to the distance of described classification boundaries is

(5-3) in extraction step (5-2), be judged as the bossing being caused by heart contraction process.

Wherein, described default classification boundaries obtains by following steps:

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

(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 the bossing caused by heart contraction, sequence

the bossing that corresponding non-heart contraction causes, and in coordinate system, take width as X-axis, take amplitude as Y-axis is by described sequence { w' _n,

show;

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

(5-1-5) bossing that calculating is caused by heart contraction is to the described center of circle (w _c, h _c) ^distancefrom D'c _n, and the bossing that non-heart contraction causes is to the described center of circle (w _c, h _c) distance

and find out distance D ' c _nmaximum corresponding point

and distance

minimum corresponding point

the mid point of 2 of finding out so 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) a point sequence { s of the bossing causing according to described heart contraction process _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) according to computing cycle T, calculate pulse frequency, wherein, pulse frequency

Further, described step (7) specifically comprises step:

(7-1) the infrared electro signal being transformed by infrared light optical signal blood oxygen detecting probe being detected is processed to (5) according to described step (1), obtains the bossing being caused by heart contraction process in infrared electro signal;

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

(7-3) 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,

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

${\mathrm{IR}}_{\mathrm{AC}}=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{IRp}}_i-\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{IRv}}_i,$

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

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

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

(7-4) according to the R value calculating, search the R value/blood oxygen saturation mapping table having 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 that blood oxygen detecting probe detects and corresponding blood oxygen saturation;

(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 to same blood oxygen saturation in sample data are averaged, blood oxygen saturation and R value average are corresponding one by one, and adopting linear interpolation to supplement blood oxygen saturation and R value, to make blood oxygen saturation spacing be 1%, thereby obtain described R value/blood oxygen saturation mapping table.

Above inventive embodiments is by identifying the feature being produced by heart contraction in signal waveform, thereby determine exactly each cycle, realize the accurate identification of pulse frequency and blood oxygen saturation, even measuring in the signal waveform baseline drift situation that loosening etc. the reason of people's hands movement or probe causes, 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, to the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skills, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.

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

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 for passing through heart contraction process feature identification pulse frequency and blood oxygen saturation that provides of the embodiment of the present invention;

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

Fig. 7 is the wave form varies figure in the one-period being caused by heart contraction process;

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 setting the step of 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.

The specific embodiment

Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, rather than whole embodiment.Embodiment based in the present invention, those of ordinary skills, not making the every other embodiment obtaining under creative work prerequisite, belong to the scope of protection of the invention.

It is a kind of by the method for heart contraction process feature identification pulse frequency and blood oxygen saturation that the embodiment of the present invention provides, and as shown in Figure 5, comprises step:

S1: the HONGGUANG signal of telecommunication being 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 heart contraction process correspondence 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 heart contraction process, differential signal sequence { b _nit is negative.In fact heart contraction process has two key characters, and the one, the time span of shrinking and the dynamics of contraction, be illustrated in 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 portion waveshape, obtain characteristic signal sequence { c _n, c wherein _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 bossings, what comprise bossing plays point sequence { s _n, terminal sequence { e _n, width sequence { w _nand amplitude sequence { h _n;

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

S6: according to the feature calculation pulse frequency of the described bossing being caused by heart contraction process;

S7: the infrared electro signal being transformed by infrared light optical signal that blood oxygen detecting probe is detected is processed to S5 according to described step S1, with the bossing that obtains being caused by heart contraction process in infrared electro signal, and in conjunction with the feature calculation blood oxygen saturation of the bossing being caused by heart contraction process in the feature of the bossing being caused by heart contraction process in described infrared electro signal and the described HONGGUANG signal of telecommunication.

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

S41: for described characteristic signal sequence { c _n, from the left side, start to find, think characteristic signal sequence { c _nfrom 0, become that to be greater than 0 point be the starting point of a bossing, think and become from being greater than 0 the terminal that 0 point is current bossing, thus extract whole bossings play point sequence { s _nand terminal sequence { e _n;

S42: the width sequence { w that extracts described whole bossings _n, w wherein _n=e _n-s _n;

S43: the amplitude sequence { h that extracts described whole bossings _n, wherein, h _n=max (Y (s _n)～Y (e _n)), 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 each bossing to the distance of described classification boundaries, if distance is greater than 0, thinks that bossing is not caused by heart contraction process, otherwise think that bossing is caused by heart contraction process, wherein, bossing to the distance of described classification boundaries is

S53: be judged as the bossing being caused by heart contraction process in extraction step S52.

Wherein, as shown in figure 11, described default classification boundaries obtains by following steps:

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

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

S513: with sequence { s' _n, { e' _n, { w' _n, { h' _nrepresent starting point, terminal, width, the amplitude sequence of the bossing caused by heart contraction, sequence

the bossing that corresponding non-heart contraction causes, and in coordinate system, take width as X-axis, take amplitude as Y-axis is by described sequence { w' _n, { h' _nand

show;

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

S514: calculate the width of all bossings that caused by heart contraction and the average of amplitude as the center of circle (w of 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: the bossing that calculating is caused by heart contraction is to the described center of circle (w _c, h _c) distance D ' c _n, and the bossing that non-heart contraction causes is to the described center of circle (w _c, h _c) distance and find out distance D ' c _nmaximum corresponding point and distance

minimum corresponding point

the mid point of 2 of finding out so 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: a point sequence { s of the bossing causing according to described heart contraction process _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: according to computing cycle T, calculate pulse frequency, wherein, pulse frequency

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

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

S72: using the starting point of the bossing being caused by heart contraction process respectively in infrared electro signal and the HONGGUANG signal of telecommunication as crest, terminal is as trough, and wherein, the crest of the HONGGUANG signal of telecommunication, trough range value sequence are designated as respectively { Rp _n, { Rv _n, the crest of infrared electro signal, trough range value sequence are designated as respectively { IRp _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,

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

${\mathrm{IR}}_{\mathrm{AC}}=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{IRp}}_i-\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{IRv}}_i,$

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

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

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

S74: search the R value/blood oxygen saturation mapping table having established according to the R value calculating, 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 that blood oxygen detecting probe detects and corresponding blood oxygen saturation;

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

S743: all R values that same blood oxygen saturation in sample data is corresponding are averaged, blood oxygen saturation and R value average are corresponding one by one, and adopting linear interpolation to supplement blood oxygen saturation and R value, to make blood oxygen saturation spacing be 1%, thereby obtain described R value/blood oxygen saturation mapping table.

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

Above disclosed is only a kind of preferred embodiment of the present invention, certainly can not limit with this interest field of the present invention, and the equivalent variations of therefore doing according to the claims in the present invention, still belongs to the scope that the present invention is contained.

Claims (7)

1. by a method for heart contraction process feature identification pulse frequency and blood oxygen saturation, it is characterized in that, comprise step:

(1) the HONGGUANG signal of telecommunication being 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;

(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 portion waveshape, obtain characteristic signal sequence { c _n, c wherein _n=-b _n(b _n≤ 0), c _n=0 (b _n> 0);

(4) find out described characteristic signal sequence { c _nsignal waveform in whole bossings, and extract the feature of described whole bossings, what comprise bossing plays point sequence { s _n, terminal sequence { e _n, width sequence { w _nand amplitude sequence { h _n;

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

(6) according to the feature calculation pulse frequency of the described bossing being caused by heart contraction process;

(7) the infrared electro signal being transformed by infrared light optical signal blood oxygen detecting probe being detected is processed to (5) according to described step (1), with the bossing that obtains being caused by heart contraction process in infrared electro signal, and in conjunction with the feature calculation blood oxygen saturation of the bossing being caused by heart contraction process in the feature of the bossing being caused by heart contraction process in described infrared electro signal and the described HONGGUANG signal of telecommunication.

2. the method for identifying pulse frequency and blood oxygen saturation by heart contraction process feature as claimed in claim 1, is characterized in that, described step (4) specifically comprises step:

(4-1) for described characteristic signal sequence { c _n, from the left side, start to find, think characteristic signal sequence { c _nfrom 0, become that to be greater than 0 point be the starting point of a bossing, think and become from being greater than 0 the terminal that 0 point is current bossing, thus extract whole bossings play point sequence { s _nand terminal sequence { e _n;

(4-2) extract the width sequence { w of described whole bossings _n, w wherein _n=e _n-s _n;

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

3. the method for identifying pulse frequency and blood oxygen saturation by heart contraction process feature as claimed in claim 1, is characterized in that, 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) calculate each bossing to the distance of described classification boundaries, if distance is greater than 0, thinks that bossing is not caused by heart contraction process, otherwise think that bossing is caused by heart contraction process, wherein, bossing to the distance of described classification boundaries is

(5-3) in extraction step (5-2), be judged as the bossing being caused by heart contraction process.

4. the method for identifying pulse frequency and blood oxygen saturation by heart contraction process feature as claimed in claim 3, is characterized in that, described default classification boundaries obtains by following steps:

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

(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 the bossing caused by heart contraction, sequence

the bossing that corresponding non-heart contraction causes, and in coordinate system, take width as X-axis, take amplitude as Y-axis is by described sequence { w' _n, { h' _nand

show;

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

(5-1-5) bossing that calculating is caused by heart contraction is to the described center of circle (w _c, h _c) distance D ' c _n, and the bossing that non-heart contraction causes is to the described center of circle (w _c, h _c) distance

and find out distance D ' c _nmaximum corresponding point and distance

minimum corresponding point

the mid point of 2 of finding out so and the distance in the center of circle are the radius r of described classification boundaries, wherein,

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

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

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

5. the method for identifying pulse frequency and blood oxygen saturation by heart contraction process feature as claimed in claim 1, is characterized in that, described step (6) specifically comprises step:

(6-1) a point sequence { s of the bossing causing according to described heart contraction process _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) according to computing cycle T, calculate pulse frequency, wherein, pulse frequency

6. the method for identifying pulse frequency and blood oxygen saturation by heart contraction process feature as claimed in claim 1, is characterized in that, described step (7) specifically comprises step:

(7-1) the infrared electro signal being transformed by infrared light optical signal blood oxygen detecting probe being detected is processed to (5) according to described step (1), obtains the bossing being caused by heart contraction process in infrared electro signal;

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

(7-3) 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,

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

${\mathrm{IR}}_{\mathrm{AC}}=\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{IRp}}_i-\frac{1}{n}\·\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{IRv}}_i,$

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

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

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

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

7. the method for identifying pulse frequency and blood oxygen saturation by heart contraction process feature as claimed in claim 6, 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 that blood oxygen detecting probe detects and corresponding blood oxygen saturation;

(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 to same blood oxygen saturation in sample data are averaged, blood oxygen saturation and R value average are corresponding one by one, and adopting linear interpolation to supplement blood oxygen saturation and R value, to make blood oxygen saturation spacing be 1%, thereby obtain described R value/blood oxygen saturation mapping table.

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