CN113576422B - Method, system and equipment for simplifying processing and quantizing physiological signal waveform - Google Patents
Method, system and equipment for simplifying processing and quantizing physiological signal waveform Download PDFInfo
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Abstract
The invention belongs to the technical field of physiological signal processing, and discloses a method, a system and equipment for simplifying and quantizing physiological signal waveforms, wherein the method comprises the following steps: filtering the acquired physiological signal oscillogram, and then carrying out normalization processing to obtain a normalized oscillogram curve; calculating the normalized oscillogram curve to generate characteristic points and generating characteristic lines and scales; and extracting a template oscillogram according to the characteristic lines, and calculating the weighted average deviation of the residual oscillogram except the template oscillogram in the physiological signal oscillogram and the characteristic point ordinate of the template oscillogram and/or the relative deviation of scales according to the template oscillogram, thereby quantifying the difference degree to identify abnormal waveforms. The method of the invention ensures that any two oscillograms have comparability in form, avoids the limitation that the traditional method judges the difference only according to the amplitude of the oscillograms and/or the calculation result of the time length difference of the pulse period, and improves the identification effect.
Description
Technical Field
The invention belongs to the technical field of physiological signal processing, and particularly relates to a method, a system and equipment for simplifying and quantizing physiological signal waveforms.
Background
The human body circulatory system consists of heart, blood vessels and blood, the process that the volume of the blood in the arteriole of the aorta or the peripheral blood vessel is pulsated and changed under the action of the heart contains a plurality of cardiovascular and microcirculatory system pathophysiological information such as hemodynamics and the like, is an important information source for researching the human body circulatory system, and is one of important bases for diagnosing diseases and evaluating whether the body functions are good or not.
The plethysmographic signal is a photoelectric detection means based on the Lambert beer law, and the principle of the plethysmographic signal is to describe the change of arterial blood volume along with the process of a cardiac cycle by utilizing a photoelectric signal, and the development process has been 80 years up to now. Because of its characteristics of no wound, continuous real-time, easy detection, many characteristic information, low cost, etc., the plethysmography is widely used in clinical or non-clinical scenes to represent various physiological parameters of human body.
The plethysmographic signal is a complex and weak physiological signal, and is easily interfered by a plurality of external factors to generate distortion, and finally, errors of physiological parameter calculation and diagnosis results are caused. Common influencing factors include: (1) subject factors such as signal self-variation, skin condition, motion of detected part, respiratory rate, electromyographic interference, premature beat noise, etc.; (2) environmental factors such as ambient light, temperature variations; (3) instrumental factors such as sensor sensitivity, baseline drift, power frequency interference, instrumental noise, etc. Therefore, before calculating physiological parameters by using the plethysmographic signal, the distortion is required to be effectively identified, measured, feature extracted, calculated and waveform corrected to obtain an accurate and high-quality plethysmographic signal.
Conventional correction methods for volume plethysmographic signals mainly include adaptive filtering methods, independent component analysis methods, wavelet transform methods, and the like. The adaptive filtering method is a method which takes the error between an observed signal and an expected signal as an index and continuously iterates to obtain an optimal signal more conforming to a physiological signal. The independent component analysis method is a method for extracting a pure signal from an original signal through multi-step iteration on the premise that a plethysmographic signal and a distorted signal are statistically independent and are not distributed in a Gaussian manner. The wavelet transform method utilizes different basis functions and scalable transform to divide the original signal into signal segments of different levels and different frequencies in time domain and frequency domain, and removes distortion according to threshold to synthesize new signal.
In the further research on the volume wave tracing technology, various comprehensive correction methods also come into play. For example, a method is proposed in the related art, in which an original signal is subjected to single period segmentation, noise is filtered by using gaussian distribution, and then a signal is reconstructed by using a trained network model. For example, there is also a related art that proposes a method of calculating a position and removing an artifact using kurtosis and skewness, calculating and synthesizing a corresponding signal feature using bayesian theorem and gaussian function, and finally connecting by a least square method. The method is that on the basis of the actually measured amplitude of the original plethysmograph signal, the extraction and statistical analysis of the designated characteristic parameters are carried out on the whole sampling wave band or the specific wave band, then the noise and the artifact are distinguished and removed according to a certain threshold value or a mathematical method, and a new signal is simulated.
In fact, the difference of the volume wave is not only expressed in the time domain and the frequency domain, but also in the normalized form. For example, two adjacent waves with smaller amplitude and period differences are not necessarily small in morphology after normalization, and a larger morphology difference may even result in a larger difference index output. The method is characterized in that the method only uses the amplitude of the original signal and the threshold range extracted periodically to judge and identify the artifact, so that certain limitation exists, even the effective signal with larger variation is mistakenly killed, and the distorted signal with larger morphological variation is missed.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art described above. Therefore, the invention provides a simplified processing and quantification method, a system and equipment for physiological signal waveforms, wherein the method ensures that the forms of any two waveform diagrams are comparable, avoids the omission of abnormal distortion and the distortion of a calculation result caused by the error killing of a correct signal only according to the calculation results of the amplitude difference or the pulse period difference of adjacent waves, and is suitable for the condition of calculating by using various-order derivative waveform diagrams.
In order to achieve the purpose, the invention adopts the following technical scheme:
a simplified processing and quantification method of physiological signal waveforms, comprising the steps of:
filtering the acquired physiological signal oscillogram, wherein the physiological signal oscillogram comprises a plurality of oscillograms, and then normalizing the oscillogram to obtain a normalized oscillogram curve;
calculating the normalized oscillogram curve to generate characteristic points and generating characteristic lines and scales thereof;
and extracting a template oscillogram according to the characteristic line and the scale thereof, and calculating the weighted average deviation of the residual oscillogram except the template oscillogram in the physiological signal oscillogram and the characteristic point ordinate of the template oscillogram and/or the relative deviation of the scale according to the template oscillogram, thereby quantifying the difference degree to identify abnormal waveforms.
In some embodiments of the invention, the physiological signal waveform is one of a plethysmographic signal waveform, an arterial barographic signal waveform, or an ultrasound arterial pulse graphic signal waveform.
In some embodiments of the invention, the acquisition system sampling rate of the acquired physiological signal waveforms must not be less than 500 hz.
In some embodiments of the present invention, the normalization process converts the waveform profile of a single pulse cycle into a dimensionless square range of one in both length and height for display.
In some embodiments of the present invention, calculating the normalized oscillogram curve to generate feature points, and generating the feature lines and the scales thereof specifically includes the following steps: calculating the center of gravity of the area under the curve of the normalized oscillogram; forming radioactive rays by taking the peak point of the normalized oscillogram curve as a starting point and penetrating through the center of gravity; calculating a slope value of the radiation; obtaining characteristic points according to the slope values; and obtaining a characteristic line and a scale thereof from the characteristic points.
In some preferred embodiments of the present invention, the waveform diagram is divided into two regions, i.e., a left region and a right region, using the abscissa corresponding to the peak point of the curve of the normalized waveform diagram as a vertical line as a boundary; the left time phase corresponding to the ascending branch of the normalized waveform graph reflects the waveform characteristics of the continuous blood volume increasing process, and the right time phase corresponding to the descending branch of the normalized waveform graph reflects the waveform characteristics of the continuous or fluctuation reducing process of the blood volume.
In some preferred embodiments of the present invention, the specific step of calculating the gravity center of the area under the curve of the normalized oscillogram is: calculating the barycentric abscissa and ordinate G0(X0, Y0) of the area under the curve of the whole normalized oscillogram; calculating the barycentric coordinates and the ordinate G1(X1, Y1) of the corresponding area of the ascending branch part of the normalized oscillogram curve; the barycentric abscissa and ordinate G2(X2, Y2) of the corresponding area of the descending branch portion of the normalized oscillogram curve are calculated.
In some embodiments of the present invention, forming the radiation through the center of gravity starting from a peak point of the normalized oscillogram curve includes: and respectively making radioactive rays from the peak point of the normalized oscillogram curve to the gravity center, the horizontal two sides of the peak point, and the starting point (0, 0) and the end point (1, 0) of the normalized oscillogram curve.
In some preferred embodiments of the present invention, forming the radiation through the center of gravity with the peak point of the normalized oscillogram curve as a starting point includes: and respectively carrying out radioactive rays from the peak point of the normalized oscillogram curve to the horizontal two sides of the G0, the G1, the G2 and the peak point and the starting point (0, 0) and the end point (1, 0) of the normalized oscillogram curve to obtain seven radioactive rays.
In some preferred embodiments of the present invention, forming the radiation through the center of gravity with the peak point of the normalized oscillogram curve as a starting point includes: and respectively making radioactive rays from the peak point of the normalized oscillogram curve to the horizontal two sides of the G1, the G2 and the peak point as well as the starting point (0, 0) and the end point (1, 0) of the normalized oscillogram curve to obtain six radioactive rays.
In some preferred embodiments of the present invention, a slope value of the radiation is calculated; obtaining the feature points according to the slope values comprises: calculating the slope values of the seven radioactive rays, and obtaining characteristic points according to the slope values; the specific process is as follows:
the ordinate of the first characteristic point is the absolute value of the slope of the horizontal radioactive ray of the wave peak point of the normalized oscillogram in the left direction, the value is zero, and the abscissa is 0;
the ordinate of the second characteristic point is the absolute value of the radioactive ray slope from the peak point of the normalized oscillogram curve to the starting point (0, 0) of the oscillogram curve, and the abscissa is 1;
the ordinate of the third characteristic point is the absolute value of the radiation slope from the peak point of the normalized oscillogram curve to G1(X1, Y1), and the abscissa is 2;
the ordinate of the fourth characteristic point is the absolute value of the radiation slope from the peak point of the normalized oscillogram curve to G0(X0, Y0), and the abscissa is 3;
the ordinate of the fifth characteristic point is the absolute value of the radiation slope from the peak point of the normalized oscillogram curve to G2(X2, Y2), and the abscissa is 4;
the ordinate of the sixth characteristic point is the absolute value of the radioactive ray slope from the peak point of the normalized oscillogram curve to the end point (1, 0) of the oscillogram curve, and the abscissa is 5;
the ordinate of the seventh characteristic point is the absolute value of the slope of the horizontal radioactive ray of the right direction of the peak point of the normalized oscillogram curve, the value is zero, and the abscissa is 6;
and drawing a line drawing of the seven characteristic points in sequence to serve as a characteristic line extracted corresponding to the physiological signal waveform.
In some preferred embodiments of the present invention, forming the radiation through the center of gravity with the peak point of the normalized oscillogram curve as a starting point includes: calculating the slope values of the six radioactive rays, wherein the specific process of obtaining the characteristic points according to the slope values is as follows:
the ordinate of the first characteristic point is the absolute value of the slope of the horizontal radioactive ray of the wave peak point of the normalized oscillogram in the left direction, the value is zero, and the abscissa is 0;
the ordinate of the second characteristic point is the absolute value of the radioactive ray slope from the peak point of the normalized oscillogram curve to the starting point (0, 0) of the oscillogram curve, and the abscissa is 1;
the ordinate of the third characteristic point is the absolute value of the radiation slope from the peak point of the normalized oscillogram curve to G1(X1, Y1), and the abscissa is 2;
the ordinate of the fourth characteristic point is the absolute value of the radiation slope from the peak point of the normalized oscillogram curve to G2(X2, Y2), and the abscissa is 3;
the ordinate of the fifth characteristic point is the absolute value of the radioactive ray slope from the peak point of the normalized oscillogram curve to the end point (1, 0) of the oscillogram curve, and the abscissa is 4;
the ordinate of the sixth characteristic point is the absolute value of the slope of the horizontal radioactive ray of the right direction of the peak point of the normalized oscillogram curve, the value is zero, and the abscissa is 5;
and drawing a line graph in sequence by using the six characteristic points as a characteristic line extracted corresponding to the waveform of the physiological signal.
In some preferred embodiments of the present invention, the line graph has a serial number of the radiation as an abscissa and an absolute value of a slope as an ordinate.
In some preferred embodiments of the present invention, the continuous probability distribution of the slope values of the radiation each approximately follows a normal distribution.
In some preferred embodiments of the present invention, the characteristic line is an envelope in which ordinate values of the start point and the end point are both zero; the scale of the characteristic line is obtained by calculating the slope of the connecting line from the gravity center of the area enveloped by the characteristic line to the starting point of the characteristic line.
In some embodiments of the present invention, the process of extracting the template oscillogram according to the characteristic line and the scale thereof is as follows: generating a characteristic line queue and a corresponding scale queue from the acquired physiological signal oscillogram queue, calculating the weighted average deviation of the vertical coordinates of the characteristic points of the characteristic line or calculating the relative deviation of the scales of the characteristic line, and selecting the oscillogram with the minimum weighted average deviation absolute value or the minimum relative deviation absolute value as the template oscillogram.
In some embodiments of the present invention, the relative deviation is a relative deviation (percentage) between the measured value of the characteristic line scale of all other oscillograms (the remaining oscillograms of the physiological signal oscillogram excluding the template oscillogram) and the corresponding scale value of the template oscillogram.
In some embodiments of the present invention, the relative deviation is a relative deviation (percentage) between an actual measurement value of the ordinate of the characteristic point of the characteristic line of all other oscillograms (residual oscillograms of the physiological signal oscillogram excluding the template oscillogram) and the ordinate of the characteristic point corresponding to the template oscillogram.
In some embodiments of the present invention, the relative deviation is calculated according to the ordinate of one of the feature points in the feature line of one of the waveforms excluding the template waveform in the waveform queue and the ordinate of the feature point corresponding to the template, for example, the 3 rd feature point ordinate of the feature line of one of the waveforms and the 3 rd feature point ordinate corresponding to the template feature line, and the calculation formula of the relative deviation of the ordinate of the feature point is as follows:
relative deviation of ordinate of certain feature point: diAnd the vertical coordinate of the actually measured characteristic point is as follows: ciAnd the ordinate of the template characteristic point: cmAnd i is the waveform diagram serial number.
In some embodiments of the invention, the calculation formula of the relative deviation of the characteristic line scale values is as follows:
relative deviation of scale values of certain characteristic lines: eiActually measuring the characteristic line scale value: siAnd template characteristic line scale value: smAnd i is the waveform diagram serial number.
In some embodiments of the present invention, the weighted average deviation is quantitatively evaluated by using a weighted average of relative deviations of all other oscillograms (residual oscillograms except the template oscillogram in the physiological signal oscillogram) and the ordinate of five feature points with serial numbers of 2-6 in the feature line of the template oscillogram, and this process can be further simplified, and the weighted average of the relative deviations of the ordinate of the five feature points is quantitatively evaluated by using the relative deviation of the scale values of the feature line instead of the weighted average of the relative deviations of the ordinate of the five feature points.
In some embodiments of the invention, the calculation formula of the weighted average deviation of the plurality of features is as follows:
multiple feature weighted mean deviation: AD, some characteristic relative deviation: djWeight of relative deviation of a certain feature: f. ofjSome characteristic number: j.
a physiological signal waveform identification system implementing a simplified processing and quantification method of a physiological signal waveform, comprising: the physiological signal waveform acquisition and processing module and the personal computer; the physiological signal waveform acquisition processing module is connected with the personal computer and outputs a waveform identification result on a display screen of the personal computer; the physiological signal waveform is one of a volume wave trace signal waveform, an arterial pressure trace signal waveform or an ultrasonic arterial pulse trace signal waveform.
A device for processing a physiological signal waveform, the physiological signal waveform being a plethysmographic waveform, the device comprising: the device comprises a personal computer and a volume wave trace signal waveform acquisition and processing module; the volume wave trace signal waveform acquisition and processing module is connected with a personal computer, acquires and processes the volume wave trace signal and transmits the volume wave trace signal to the personal computer for processing and computational analysis, and the personal computer identifies abnormal waveforms by using the simplified processing and quantification method of the physiological signal waveform and outputs identification waveform results on a display screen of the personal computer.
In some embodiments of the invention, the plethysmographic signal waveform acquisition processing module comprises a plethysmographic signal sensor.
In some embodiments of the present invention, the personal computer is internally provided with a parameter setting and signal processing and computational analysis module, which includes a normalization processing module, a characteristic line generation and calibration module, a template oscillogram selection module, a waveform identification and identification module, and a result statistics output module.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, after the physiological signals are normalized on the basis of filtering the periodic beating physiological signals, the morphological difference of the signals is quantized to realize abnormal distorted waveform identification. The method ensures that the forms of any two oscillograms are comparable, avoids the distortion of the calculation result caused by the false killing of correct signals and the missing judgment of abnormal distortion only according to the calculation result of the amplitude difference and/or the pulse period difference of adjacent waves, and is also suitable for the condition of calculating by using the oscillograms of derivatives of various orders.
2. After probability distribution statistical analysis is carried out on the vertical coordinates of a plurality of actually measured characteristic points of the normalized periodic beating physiological signals, one oscillogram signal with relatively highest comprehensive index conformity in a queue is selected as a comparison template oscillogram so as to quantify and identify abnormal distorted waveforms. The method can effectively avoid the situation that the virtual oscillogram with the absolute highest index conformity is taken as the template, which causes the mismatching of various indexes of the oscillograms in the actually acquired oscillogram queue and generates larger calculation deviation.
3. The invention relates to morphological difference analysis after normalization of physiological signals of periodic beats and identification of abnormal distorted oscillograms possibly existing in the morphological difference analysis. Meanwhile, the method supports the user to set the deviation allowable range of the weighted average calculation formula of the relative deviation of the multiple characteristics and the judgment abnormity, is not contradictory to other signal quality evaluation methods or signal correction processing methods, and can be used in a matching way.
4. The invention can quickly and real-timely identify abnormal distorted waveforms according to morphological difference and quantitative index range of special characteristic lines generated by actually acquired physiological signals, can be used for evaluating the repeatability and stability of a physiological signal waveform acquisition device, and can also be used for describing the dynamic change rule of a photoelectric plethysmography signal so as to read the contained physiological and pathological information, thereby greatly improving the performance of medical detection equipment, being beneficial to improving the signal processing scientificity of a new generation of intelligent medical instrument, and further promoting the innovation of an intelligent medical detection technology and the development of a new generation of intelligent physical and mental health detection instrument.
Drawings
FIG. 1 is a waveform diagram before normalization in example 1 of the present invention;
FIG. 2 is a normalized waveform diagram according to example 1 of the present invention;
fig. 3 is an area barycenter abscissa and ordinate corresponding to the area under the normalized oscillogram curve of embodiment 1 of the present invention, and a radiation diagram is made with the peak point as the starting point;
FIG. 4 is a normal distribution graph of slope versus deviation of the normalized waveform of example 1 of the present invention;
fig. 5 is a characteristic line diagram extracted from a physiological signal waveform according to embodiment 1 of the present invention;
FIG. 6 is a waveform of example 1 of the present invention;
FIG. 7 is a characteristic line graph of typical waveforms of samples of different ages and health conditions in example 1 of the present invention;
FIG. 8 is a graph of the absolute value of the relative deviation of the scale values of 130 waveforms of one person in accordance with embodiment 1 of the present invention;
FIG. 9a is a graph of the absolute values of the weighted average deviations of the ordinate using 5 feature points for 130 waveforms in example 1 of the present invention;
FIG. 9b is a graph showing the calculation result of the relative deviation of the scale values of the feature lines of 5 feature points of the 130 waveform diagrams in example 1 of the present invention;
fig. 10 is a graph illustrating a correlation analysis performed on the weighted average deviation of the ordinate of the feature point of the feature line and the relative deviation of the scale value of the feature line according to embodiment 1 of the present invention;
FIG. 11 is an Xp mean value graph of the first 65 waveform diagrams and the last 65 waveform diagrams of 130 waveform diagrams of a person in embodiment 1 of the present invention;
FIG. 12 is a graph of the absolute values of the relative deviations of the scale values of the first 65 waveforms of the 130 waveforms of one person in accordance with embodiment 1 of the present invention;
FIG. 13 is a graph of the absolute value of the relative deviation of the scale values of the characteristic lines of the last 65 waveforms of 130 waveforms of one person in accordance with embodiment 1 of the present invention;
FIG. 14 is the average Xp values after the distortion removal of the first 65 waveforms and the second 65 waveforms of the 130 waveforms of one person in embodiment 1 of the present invention;
FIG. 15 is a graph showing the calculation of RI values according to example 2 of the present invention;
fig. 16 is an area barycenter abscissa and ordinate corresponding to the area under the normalized oscillogram curve respectively, and a radiation diagram is made with the peak point as the starting point in accordance with embodiment 2 of the present invention;
FIG. 17 is a waveform diagram of the minimum value of the weighted average deviation of the ordinate of 4 feature points in 130 waveform diagrams according to embodiment 2 of the present invention;
FIG. 18a is a graph of the absolute values of the weighted average deviations of the ordinate using 4 feature points for 130 waveforms in example 2 of the present invention;
FIG. 18b is a graph showing the calculation result of the relative deviation of the scale values of the feature lines of 4 feature points of 130 waveforms in example 2 according to the present invention;
fig. 19 is a graph illustrating a correlation analysis performed on the weighted average deviation of the ordinate of the feature point of the feature line and the relative deviation of the scale value of the feature line according to embodiment 2 of the present invention;
FIG. 20 is an RI average value plot of the first 65 waveform plots and the second 65 waveform plots of 130 waveform plots of a person in accordance with embodiment 2 of the present invention;
FIG. 21 is a graph of the absolute value of the relative deviation of the scale values of the first 65 waveforms of the 130 waveforms of one person in accordance with embodiment 2 of the present invention;
FIG. 22 is a graph of the absolute value of the relative deviation of the scale values of the characteristic lines of the last 65 waveforms of 130 waveforms of one person in accordance with embodiment 2 of the present invention;
fig. 23 is an RI average value graph after distortion removal of the first 65 waveform diagrams and the second 65 waveform diagrams of 130 waveform diagrams of a person in embodiment 2 of the present invention.
Detailed Description
The concept and technical effects of the present invention will be clearly and completely described below in conjunction with the embodiments to fully understand the objects, features and effects of the present invention. It is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and those skilled in the art can obtain other embodiments without inventive effort based on the embodiments of the present invention, and all embodiments are within the protection scope of the present invention.
Example 1
A method of processing a plethysmographic signal, comprising the steps of:
extracting the waveform of the volume wave tracing signal by using an acquisition system with the sampling rate of 500 Hz, carrying out filtering processing on the acquired volume wave tracing signal for 5 minutes, and then carrying out normalization processing on a physiological signal oscillogram which comprises a plurality of oscillograms to obtain a normalized oscillogram curve queue;
calculating the normalized oscillogram curve to generate characteristic points and generating characteristic lines and scales thereof;
and extracting a template oscillogram according to the characteristic lines and the scales thereof, and calculating the weighted average deviation of the residual oscillogram of the physiological signal oscillogram except the template oscillogram and the characteristic point ordinate of the template oscillogram and/or the relative deviation of the scales according to the template oscillogram, thereby quantifying the difference degree to identify abnormal waveforms.
In this embodiment: the specific process of normalization treatment is as follows: the waveform curve of a single pulse period is converted into a dimensionless square range with the length and the height of one for display.
FIG. 1: waveform before normalization, fig. 2: normalized waveform plot.
In this embodiment: calculating the normalized oscillogram curve to generate characteristic points, and generating characteristic lines and scales thereof specifically comprises the following steps: calculating the gravity center of the area under the curve of the normalized oscillogram; forming radioactive rays by taking the peak point of the curve of the normalized oscillogram as a starting point and passing through the center of gravity; calculating the slope value of the radioactive rays; obtaining characteristic points according to the slope values; and obtaining a characteristic line and a scale thereof from the characteristic points.
In this embodiment: the concrete steps of calculating the gravity center of the area under the curve of the normalized oscillogram are as follows: calculating the barycentric abscissa and ordinate G0(X0, Y0) of the area under the curve of the whole normalized oscillogram; calculating the abscissa and ordinate G1(X1, Y1) of the area barycenter corresponding to the ascending part of the curve of the normalized oscillogram; the abscissa and ordinate G2(X2, Y2) of the area barycenter corresponding to the falling portion of the normalized oscillogram curve are calculated. Dividing the oscillogram into a left area and a right area by taking the abscissa corresponding to the peak point of the curve of the normalized oscillogram as a vertical line as a boundary; the left time phase corresponding to the ascending branch of the normalized waveform graph reflects the waveform characteristics of the continuous blood volume increasing process, and the right time phase corresponding to the descending branch of the normalized waveform graph reflects the waveform characteristics of the continuous or fluctuating blood volume decreasing process.
In this embodiment: forming the radiation rays through the center of gravity with a peak point of the normalized oscillogram curve as a starting point includes: seven radiation rays are obtained by applying radiation to both horizontal sides of the peak point of the normalized oscillogram curve from the peak point of the normalized oscillogram curve to G0, G1, G2 and the peak point, and to the start point (0, 0) and the end point (1, 0) of the normalized oscillogram curve, respectively (as shown in fig. 3).
In this embodiment: calculating the slope value of the radioactive rays; obtaining the feature points according to the slope values comprises: calculating the slope values of the seven radioactive rays, and obtaining characteristic points according to the slope values, wherein the concrete process comprises the following steps: the ordinate of the first characteristic point is the absolute value of the slope of the horizontal radioactive ray of the peak point of the normalized oscillogram curve in the left direction, the value is zero, and the abscissa is 0; the ordinate of the second characteristic point is the absolute value of the radioactive ray slope from the peak point of the normalized oscillogram curve to the starting point (0, 0) of the oscillogram curve, and the abscissa is 1; the ordinate of the third feature point is the absolute value of the radiation slope from the peak point of the normalized oscillogram curve to G1(X1, Y1), and the abscissa is 2; the ordinate of the fourth characteristic point is the absolute value of the radiation slope from the peak point of the normalized oscillogram curve to G0(X0, Y0), and the abscissa is 3; the ordinate of the fifth feature point is the absolute value of the radiation slope from the peak point of the normalized oscillogram curve to G2(X2, Y2), and the abscissa is 4; the ordinate of the sixth characteristic point is the absolute value of the radioactive ray slope from the peak point of the normalized oscillogram curve to the end point (1, 0) of the oscillogram curve, and the abscissa is 5; the ordinate of the seventh characteristic point is the absolute value of the slope of the horizontal radioactive ray of the peak point of the normalized oscillogram curve in the right direction, the value is zero, and the abscissa is 6; a line graph is sequentially drawn at equal intervals by the seven feature points to serve as a feature line extracted corresponding to the physiological signal waveform, as shown in fig. 5 (the ordinate represents the numerical value of the ordinate of the feature point, and the abscissa is the serial number of the radioactive ray).
In this embodiment, the measured oscillogram queue imported by the user is normalized and then 5 slope values are calculated, and all the 5 slope values follow normal distribution. FIG. 4 (with the ordinate representing the probability of the Xp value appearing and the abscissa representing the Xp value distribution range) is a normal distribution diagram of the relative deviation of one of the slope values.
In this embodiment, the scale of the feature point is defined as follows: the scale of the characteristic line is the slope from the center of gravity of the area enveloped by the characteristic line to the starting point of the characteristic line.
In this embodiment, typical waveforms from samples of different ages and health conditions are assigned scale values, and as shown in fig. 6, it can be found that the waveforms of different ages and health conditions are different, and the obtained scale values of the characteristic line are also different. The characteristic of assigning scale values is that any normalized wave curve can be assigned with a quantitative scale for comparing and analyzing the difference of two wave curves, and the scale can be used for carrying out statistical operation on the change of morphological characteristics. Characteristic lines of typical waveforms from samples of different ages and health conditions are shown in fig. 7 (the ordinate represents the numerical value of the ordinate of the characteristic point, and the abscissa is the serial number of the characteristic point).
In this embodiment, the specific process of extracting the template oscillogram according to the characteristic line, and calculating the weighted average deviation of the physiological signal oscillogram excluding the longitudinal coordinates of the template oscillogram and the single characteristic point of the template oscillogram and/or the relative deviation of the scales thereof according to the template oscillogram, so as to quantify the difference degree to identify the abnormal waveform includes: the 130 continuous oscillogram queues collected in a certain actual test of the same subject are imported into a system, the oscillogram with the minimum weighted average deviation of the vertical coordinates of five characteristic points and/or the absolute value of the relative deviation of scales in the 130 oscillograms is automatically calculated, the oscillogram is identified as the template of the oscillogram queue, as shown in fig. 8 (the vertical coordinate represents the absolute value of the relative deviation of scale values of characteristic lines, and the horizontal coordinate is the serial number of the oscillograms in the queue), the weighted average deviation of the vertical coordinates of the characteristic points and/or the relative deviation of the scales of each of the rest oscillograms and the template are further calculated, and the difference is quantized so as to identify abnormal waveforms.
In this embodiment, the weighted average deviation of the ordinate of 5 feature points of 130 oscillogram feature lines is compared with the calculation result of the relative deviation of the scale values of the feature lines, as shown in fig. 9a and 9b, the ordinate of fig. 9a represents the weighted average deviation of the ordinate of the feature points, the ordinate of fig. 9b represents the absolute value of the relative deviation of the scale values of the feature lines, and the abscissa of fig. 9a and 9b is the serial number of the oscillogram in the queue. From fig. 9a and 9b, it can be seen that: the trends of the two curves are highly coincident, the arrow points to two lowest values in the whole volume wave queue, namely the weighted average deviation of the characteristic line multinomial slope and the relative deviation of the characteristic line scale value reach the lowest value at the same time, namely the two calculation results select the similar oscillogram as a template, and the oscillogram can be used as the template.
The relative deviation calculation formula of the characteristic line scale values is as follows:
relative deviation of scale values of certain characteristic lines: eiActually measuring the characteristic line scale value: siAnd template characteristic line scale value: smAnd i is the waveform diagram serial number.
Based on the user preset weights, the system automatically calculates a weighted average deviation (based on five slopes).
The formula:
feature line scale weighted mean deviation: AD, relative deviation of ordinate of a certain feature point: djWeight of ordinate of a certain feature point: f. ofjAnd the serial number of the characteristic point: j.
in this embodiment, correlation analysis is performed on the calculation result of the relative deviation between the weighted average deviation of the ordinate of the feature line and the scale value of the feature line, and the correlation coefficient is 0.93, as shown in fig. 10 (the ordinate represents the absolute value of the relative deviation of the scale value of the feature line, and the abscissa represents the reduced absolute value of the weighted average deviation of the ordinate of the feature point), the scale value of the feature line well represents the morphological information included in the feature line. The description characteristic line fuses each local complex change of the wave curve, reflects the form of the characteristic line, and well reflects the form of the characteristic line on the scale value, and the scale value of the characteristic line is more concise and convenient to quantify the form of the characteristic line.
In this example, we further consider the first 65 waveforms and the second 65 waveforms as two consecutive test results of the same subject, test 1 and test 2, respectively. The Xp values (abscissa values of the peak points of the normalized oscillogram curve) are used as output results to show that the Xp mean values of the two tests are respectively as follows: 0.264 and 0.273, as shown in FIG. 11 (ordinate represents Xp value). It can be seen from fig. 11 that the average values Xp of the test 1 (left) and the test 2 (right) fluctuate greatly, and the average values of the two tests are different greatly, so that a certain distorted waveform may exist in the waveform diagram. Then, respectively selecting the template based on the characteristic line scales for the two test results, as shown in fig. 12 and fig. 13 (the ordinate represents the absolute value of the relative deviation of the scale values of the characteristic lines, and the abscissa is the serial number of the oscillogram in the queue), calculating the relative deviation of the template and the scales of the other oscillograms, setting the range value of the identification threshold to be 0.15, identifying the waveform beyond the range as distortion, and after removing the distorted waveform, respectively setting the Xp mean values of the two tests as: 0.272 and 0.275, specifically as shown in fig. 14 (ordinate represents Xp value), it can be seen from the above fig. 14 that, after the distorted waveform is removed, Xp mean values of test 1 (left) and test 2 (right) become more stable, the mean value difference between the two previous and next test results is obviously reduced, and the repeatability and stability are obviously improved.
Therefore, the abnormal waveforms can be identified by the processing method, so that the stability and the repeatability of a waveform identification system and equipment are improved, the performance of a medical detection instrument is greatly improved, the signal processing performance of a new-generation intelligent medical instrument and the scientificity of a detection result are favorably improved, especially, a medical detection terminal closely related to medical big data and health cloud computing plays an important role in ensuring the validity of the detection result in a scene without the operation of a professional doctor, and the innovation of an intelligent medical detection technology and the performance improvement and the popularization of a new-generation intelligent physical and mental health detection instrument are further promoted.
Example 2
The difference from example 1 is that: the radiation is made from the peak point of the normalized oscillogram curve to the horizontal sides of G1, G2, the peak point, and the start point (0, 0) and the end point (1, 0) of the normalized oscillogram curve, respectively, to obtain six radiation rays, as shown in fig. 16 (the ordinate represents the amplitude, and the abscissa represents the pulse period). The radiation from the peak point of the normalized oscillogram curve to G0 is removed.
The specific process of obtaining the characteristic points according to the slope values is as follows: the ordinate of the first characteristic point is the absolute value of the slope of the horizontal radioactive ray of the peak point of the normalized oscillogram curve in the left direction, the value is zero, and the abscissa is 0; the ordinate of the second characteristic point is the absolute value of the radioactive ray slope from the peak point of the normalized oscillogram curve to the starting point (0, 0) of the oscillogram curve, and the abscissa is 1; the ordinate of the third feature point is the absolute value of the radiation slope from the peak point of the normalized oscillogram curve to G1(X1, Y1), and the abscissa is 2; the ordinate of the fourth characteristic point is the absolute value of the radiation slope from the peak point of the normalized oscillogram curve to G2(X2, Y2), and the abscissa is 3; the ordinate of the fifth characteristic point is the absolute value of the radioactive ray slope from the peak point of the normalized oscillogram curve to the end point (1, 0) of the oscillogram curve, and the abscissa is 4; the ordinate of the sixth characteristic point is the absolute value of the slope of the horizontal radioactive ray of the right direction of the peak point of the normalized oscillogram curve, the value is zero, and the abscissa is 5; and drawing a line graph at equal intervals in sequence by using the six characteristic points as a characteristic line extracted corresponding to the waveform of the physiological signal.
In this embodiment, the specific process of extracting the template oscillogram according to the characteristic line, and calculating the weighted average deviation of the ordinate of the single characteristic point of the template oscillogram or the relative deviation of the scale values of the characteristic line in the physiological signal oscillogram according to the template oscillogram, so as to quantify the difference degree to identify the abnormal waveform, includes: the 130 continuous oscillogram queues collected in a certain actual test of the same subject are imported into a system, the oscillogram with the minimum absolute value of the weighted average deviation of the ordinate of the characteristic points in the 130 oscillograms or the relative deviation of the scale values of the calculated characteristic lines is automatically calculated, the oscillogram is identified as the template of the oscillogram queue, as shown in fig. 17 (the ordinate represents the absolute value of the weighted average deviation of the ordinate of the characteristic points, and the abscissa is the serial number of the oscillograms in the queue), the weighted average deviation of the ordinate of each of the other oscillograms and the characteristic points of the template or the relative deviation of the scale values of the calculated characteristic lines is further calculated, and therefore the difference is quantified to identify abnormal waveforms.
In this embodiment, the weighted average deviation of the ordinate of the 4 feature points of the 130 waveform diagram feature lines is compared with the calculation result of the relative deviation of the feature line scales, as shown in fig. 18a (the ordinate represents the weighted average deviation of the ordinate of the feature points, and the abscissa represents the serial number of the waveform diagram in the queue) and fig. 18b (the ordinate represents the absolute value of the relative deviation of the scale values of the feature lines, and the abscissa represents the serial number of the waveform diagram in the queue). From fig. 18a and 18b, it can be seen that: the trends of the two curves are highly coincident, arrows point to the whole volume wave queue, and the two methods finally select the similar waveform as a template, so that the waveform can be used as the template.
The relative deviation formula of the characteristic line scale values:
relative deviation of scale values of certain characteristic lines: eiActually measuring the characteristic line scale value: siAnd template characteristic line scale value: smAnd i is the waveform diagram serial number.
Based on the user preset weights, the system automatically calculates a weighted average deviation (based on four slopes).
The formula:
feature line scale weighted mean deviation: AD, relative deviation of ordinate of a certain feature point: djWeight of ordinate of a certain feature point: f. ofjAnd the serial number of the characteristic point: j.
in this embodiment, correlation analysis is performed on the calculation result of the relative deviation between the weighted average deviation of the ordinate of the multiple feature points of the feature line and the scale value of the feature line, and the correlation coefficient is 0.974, as shown in fig. 19 (the ordinate represents the absolute value of the weighted average deviation of the ordinate of the feature point, and the abscissa represents the absolute value of the relative deviation of the scale value of the feature line), the scale value of the feature line well represents the morphological information included in the feature line. The above data analysis proves that the characteristic lines and scales generated by 6 radioactive rays achieve almost the same effect as the characteristic lines and scales generated by 7 radioactive rays, and can be used for extracting the actual measurement oscillogram template and identifying the abnormal oscillogram, and so on, the characteristic lines generated by increasing and decreasing the characteristic points forming the characteristic lines are all included in the protection scope of the invention.
In this example, we further consider the first 65 waveforms and the second 65 waveforms as two consecutive test results of the same subject, test 1 and test 2, respectively. The RI (RI) (RI represents the Reflection Index of the artery, and the ratio of the peak point (peak point height B) of the reflected wave to the peak point (peak point height a) of the incident wave can be obtained (B/a) to reflect the tension of the blood vessel) as an output result, and the RI value calculation graph is shown in the graph of A (one way of obtaining B), B (the other way of obtaining B), C (a reference line of obtaining B for A), and D (the ordinate of A and B represents the amplitude value after the actual sampling value normalization of the oscillogram, and the abscissa is the time point after the sampling point time normalization, the ordinate of C and D represents the first derivative of the amplitude value after the actual sampling value normalization of the oscillogram, and the abscissa is the time point after the sampling point time normalization) in FIG. 15; the RI averages of the two tests are respectively: 0.492, 0.472 as shown in fig. 20 (ordinate represents RI value, abscissa is sequence number of waveform map in queue). It can be seen from fig. 20 that the results of RI mean values of test 1 (left) and test 2 (right) fluctuate greatly, and the mean values of the two tests are different greatly, so that a certain distorted waveform may exist in the waveform diagram. And then respectively selecting the template based on the characteristic line scales for the two test results, as shown in a graph 21 (the ordinate represents the relative deviation of the scale values of the characteristic lines, and the abscissa represents the serial numbers of the oscillograms in the queue) and a graph 22 (the ordinate represents the absolute value of the relative deviation of the scale values of the characteristic lines, and the abscissa represents the serial numbers of the oscillograms in the queue), calculating the relative deviation of the template and the scales of the other oscillograms, setting the range value of the identification threshold to be 0.15, identifying the waveform exceeding the range as distortion, and after removing the distorted waveform, respectively setting the RI mean values of the two tests as: 0.465 and 0.466, specifically as shown in fig. 23 (the ordinate represents the RI value), it can be seen from the above fig. 23 that after the distorted waveform is removed, the RI mean values of the test 1 (left) and the test 2 (right) become more stable, the mean value difference between the two test results is obviously reduced, and the repeatability and the stability are obviously improved.
The significance of the embodiment is to further explain the effectiveness of the waveform identification system on abnormal waveform identification and the effect of improving the repeatability and stability of the test from the application perspective through the output index (RI) which does not relate to the ordinate calculation process of the feature point.
The apparatus for processing a waveform of a plethysmographic signal of the present embodiment comprises: the device comprises a personal computer and a volume wave trace signal waveform acquisition and processing module; the volume wave trace signal waveform acquisition processing module is connected with the personal computer, acquires and processes the volume wave trace signal and transmits the volume wave trace signal into the personal computer for processing, calculation and analysis, identifies abnormal waveforms according to a preset threshold range, and outputs identification waveform results on a display screen of the personal computer.
The volume wave trace signal waveform acquisition and processing module comprises a volume wave trace signal sensor.
The personal computer is internally provided with a parameter setting, signal processing, calculating and analyzing module which comprises a normalization processing module, a calculating and scale giving module, an extraction template oscillogram, a waveform identification module and a result output module.
Volume plethysmographic signal sensor: when the luminous tube irradiates a light beam with a certain wavelength to the surface of the skin at the finger tip, the light beam is detected by the receiving tube in a transmission or reflection mode, in the process, the light intensity detected by the receiving tube is weakened due to the absorption attenuation effect of tissues (skin muscles and blood) at the finger tip, wherein the absorption of the skin muscles and the like to the light is kept constant in the whole blood circulation, the blood volume presents periodic pulsating change under the action of the heart, the peripheral blood volume is the largest when the heart contracts, the light absorption amount is the largest, and the detected light intensity is the smallest; when the heart is in diastole, on the contrary, the detected light intensity is the maximum, so the receiving tube outputs the detected light intensity in a periodic pulsating change, and the light intensity change signal can be converted into an electric signal, namely a volume plethysmographic signal through a series of processing of the system.
A normalization processing module: the method comprises the steps of performing baseline level correction on the starting point and the end point of a waveform of a volume wave trace signal, performing amplitude normalization by taking a waveform peak point as a maximum value after correction, performing time normalization by taking a cardiac cycle as a time length, and solving a first derivative of a normalized graph to form a blood volume change speed curve.
A calculate and assign scale module: and extracting characteristic points from the normalized oscillogram, generating characteristic lines and giving corresponding scales to the states of the characteristic lines.
Extracting a template oscillogram: based on a section of oscillogram of the imported measured oscillogram queue, the system automatically calculates the oscillogram, generates a characteristic line for each normalized volume wave and gives corresponding scales to the characteristic line.
According to user preset: (1) the system automatically compares the weighted average deviation of the vertical coordinates of each characteristic point forming the characteristic line and automatically calculates the oscillogram of the minimum value of the weighted average deviation absolute values of the vertical coordinates of 5 characteristic points in the oscillogram; or (2) the system automatically compares the relative deviation of the scale values of the characteristic line and automatically finds the oscillogram with the minimum absolute value of the relative deviation of the scale values of the characteristic line, and the oscillogram is identified as the template of the section of the oscillogram queue.
A waveform identification module: (1) calculating the relative deviation (percentage) between the ordinate of the measured characteristic point of all other oscillograms (residual oscillograms except the template oscillogram in the physiological signal oscillogram) and the ordinate of the corresponding characteristic point of the template and the weighted average deviation of the ordinates of a plurality of characteristic points, identifying the oscillogram which exceeds the allowable range as an abnormal distortion oscillogram and displaying the oscillogram with different colors; or (2) calculating the relative deviation between the scale value of the measured characteristic line of all other oscillograms (residual oscillograms except the template oscillogram in the physiological signal oscillogram) and the scale value of the template, identifying the oscillogram which is beyond the allowable range as an abnormal distortion oscillogram signal and displaying the oscillogram with different colors.
A result output module: and outputting all the waveforms of the volume wave tracing signal (the waveforms of the volume wave tracing signal comprise template extraction, normal and abnormal distorted waveforms) and statistical results (such as the total waveform number, the number of abnormal waveforms, the number of normal waveforms and the like) of the waveforms.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention. Furthermore, the embodiments of the present invention and the features of the embodiments may be combined with each other without conflict.
Claims (8)
1. A simplified method of processing and quantizing a physiological signal waveform, comprising the steps of:
filtering the acquired physiological signal oscillogram, wherein the physiological signal oscillogram comprises a plurality of oscillograms, and then normalizing the oscillogram to obtain a normalized oscillogram curve;
calculating the normalized oscillogram curve to generate characteristic points and generating characteristic lines and scales thereof;
extracting a template oscillogram according to the characteristic line and the scale thereof, and calculating the weighted average deviation of the residual oscillogram except the template oscillogram in the physiological signal oscillogram and the characteristic point ordinate of the template oscillogram and/or the relative deviation of the scale according to the template oscillogram so as to quantify the difference degree to identify abnormal waveforms; calculating the normalized oscillogram curve to generate characteristic points, wherein the generation of characteristic lines and scales thereof specifically comprises the following steps: calculating the center of gravity of the area under the curve of the normalized oscillogram; forming radioactive rays by taking the peak point of the normalized oscillogram curve as a starting point and penetrating through the center of gravity; calculating a slope value of the radiation; obtaining characteristic points according to the slope values; obtaining a characteristic line and a scale thereof from the characteristic points; drawing a line graph in sequence by the characteristic points as characteristic lines extracted corresponding to the waveforms of the physiological signals; the scale of the characteristic line is obtained by calculating the slope of the connecting line from the gravity center of the area enveloped by the characteristic line to the starting point of the characteristic line.
2. The simplified processing and quantization method of claim 1, wherein the step of computing the center of gravity of the area under the curve of the normalized oscillogram comprises: calculating the barycentric abscissa and ordinate G0(X0, Y0) of the area under the curve of the whole normalized oscillogram; calculating the barycentric coordinates and the ordinate G1(X1, Y1) of the corresponding area of the ascending branch part of the normalized oscillogram curve; the barycentric abscissa and ordinate G2(X2, Y2) of the corresponding area of the descending branch portion of the normalized oscillogram curve are calculated.
3. The simplified processing and quantization method of claim 1, wherein forming a radiation ray through the center of gravity starting at a peak point of the normalized wave plot curve comprises: and respectively making radioactive rays from the peak point of the normalized oscillogram curve to the gravity center, the horizontal two sides of the peak point, and the starting point (0, 0) and the end point (1, 0) of the normalized oscillogram curve.
4. The simplified processing and quantization method of claim 2, wherein forming a radial line through the center of gravity starting at a peak point of the normalized wave plot curve comprises: and respectively carrying out radioactive rays from the peak point of the normalized oscillogram curve to the horizontal two sides of the G0, the G1, the G2 and the peak point and the starting point (0, 0) and the end point (1, 0) of the normalized oscillogram curve to obtain seven radioactive rays.
5. The simplified processing and quantification method according to claim 4, characterized in that a slope value of the radiation is calculated; obtaining the feature points according to the slope values comprises: calculating the slope values of the seven radioactive rays, and obtaining characteristic points according to the slope values; the specific process is as follows:
the ordinate of the first characteristic point is the absolute value of the slope of the horizontal radioactive ray of the wave peak point of the normalized oscillogram in the left direction, the value is zero, and the abscissa is 0;
the ordinate of the second characteristic point is the absolute value of the radioactive ray slope from the peak point of the normalized oscillogram curve to the starting point (0, 0) of the oscillogram curve, and the abscissa is 1;
the ordinate of the third characteristic point is the absolute value of the radiation slope from the peak point of the normalized oscillogram curve to G1(X1, Y1), and the abscissa is 2;
the ordinate of the fourth characteristic point is the absolute value of the radiation slope from the peak point of the normalized oscillogram curve to G0(X0, Y0), and the abscissa is 3;
the ordinate of the fifth characteristic point is the absolute value of the radiation slope from the peak point of the normalized oscillogram curve to G2(X2, Y2), and the abscissa is 4;
the ordinate of the sixth characteristic point is the absolute value of the radioactive ray slope from the peak point of the normalized oscillogram curve to the end point (1, 0) of the oscillogram curve, and the abscissa is 5;
the ordinate of the seventh characteristic point is the absolute value of the slope of the horizontal radioactive ray of the right direction of the peak point of the normalized oscillogram curve, the value is zero, and the abscissa is 6;
and drawing a line drawing of the seven characteristic points in sequence to serve as a characteristic line extracted corresponding to the physiological signal waveform.
6. The simplified processing and quantization method of claim 1, wherein the process of extracting the template waveform from the feature lines and their scales is: generating a characteristic line queue and a corresponding scale queue from the acquired physiological signal oscillogram queue, calculating the weighted average deviation of the vertical coordinates of the characteristic points of the characteristic line or calculating the relative deviation of the scales of the characteristic line, and selecting the oscillogram with the minimum weighted average deviation absolute value or the minimum relative deviation absolute value as the template oscillogram.
7. A physiological signal waveform identification system implementing the simplified processing and quantization method of a physiological signal waveform of any one of claims 1-6, comprising: the physiological signal waveform acquisition and processing module and the personal computer; the physiological signal waveform acquisition processing module is connected with the personal computer and outputs a waveform identification result on a display screen of the personal computer; the physiological signal waveform is one of a volume wave trace signal waveform, an arterial pressure trace signal waveform or an ultrasonic arterial pulse trace signal waveform.
8. A device for processing a physiological signal waveform, wherein the physiological signal waveform is a plethysmographic waveform, the device comprising a personal computer and a plethysmographic signal waveform acquisition processing module; the waveform acquisition and processing module of the plethysmographic signal is connected with a personal computer, acquires and processes the plethysmographic signal and transmits the plethysmographic signal to the personal computer for processing and computational analysis, the personal computer identifies abnormal waveforms by using the simplified processing and quantification method of physiological signal waveforms of any one of claims 1 to 6, and outputs the identification waveform result on a display screen of the personal computer.
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| CN101332084A (en) * | 2007-06-28 | 2008-12-31 | 罗晓民 | Area barycenter displacement analytical method of pulse wave |
| US8165664B1 (en) * | 2007-10-30 | 2012-04-24 | Pacesetter, Inc. | Systems and methods for increased specificity in diagnostics |
| CN101703396A (en) * | 2009-11-06 | 2010-05-12 | 中国科学院合肥物质科学研究院 | Radial artery pulse wave based cardiovascular function parameter detection and analysis method and detection device |
| CN104834921A (en) * | 2015-05-22 | 2015-08-12 | 清华大学深圳研究生院 | Electrocardio normality/abnormality big-data processing method and device |
| CN111578983A (en) * | 2019-02-15 | 2020-08-25 | 瑞萨电子株式会社 | Abnormality detection device, abnormality detection system, and abnormality detection method |
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