CN114795140B - Myocardial work index monitoring system and method based on cardiac shock signal - Google Patents
Myocardial work index monitoring system and method based on cardiac shock signal Download PDFInfo
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Abstract
The invention discloses a myocardial work index monitoring system based on a cardiac shock signal, which comprises a cardiac shock signal sensing module, a circuit processing module, a signal digital processing module and a signal analysis module.
Description
Technical Field
The invention relates to the technical field of human vital sign monitoring, in particular to a myocardial work index monitoring system and method based on a cardiac shock signal.
Background
Cardiovascular diseases have become the first killers threatening human health. Early prevention, early diagnosis and early treatment of cardiovascular diseases are one of the most important issues in the medical field. Therefore, the importance of daily real-time monitoring of the heart function is great, the monitoring finds problems, the timely intervention is carried out, the cardiovascular disease is killed in the sprouting stage, the treatment difficulty is greatly reduced, and the life quality of the prognosis of a patient is improved.
The myocardial work index (Tei index), also known as the myocardial synthesis index, was first proposed by the physician Chuwa Tei. It refers to the ratio of the sum of ventricular Isovolumetric Contraction Time (ICT) and Isovolumetric Relaxation Time (IRT) to Ejection Time (ET). Is one of the indexes reflecting the whole function of the heart. Studies have shown that this index increases as heart function decreases, and this feature can be used to effectively detect problems with decreased heart function.
The traditional Tei index measurement method adopts the pulse Doppler ultrasound scanning mitral valve diastolic blood flow spectrum and left ventricle outflow blood flow spectrum in different cardiac cycles to obtain calculation parameters. The test result of the method depends on the manual selection of the mitral valve position by an ultrasonic operator and the influence of the frequency spectrum identification capability, and the ultrasonic equipment is not easy to carry and is not convenient for monitoring the heart function in real time.
Disclosure of Invention
The invention aims to provide a myocardial work index monitoring system and method based on a cardiac shock signal, the system and method are combined with the requirements of practical application, a convenient acquisition module is designed, interference components in the signal are effectively removed through signal processing, and finally relevant parameters required by calculation of the myocardial work index are extracted through medical verification, so that the requirements of remote medical treatment can be met, and early warning on cardiac abnormality can be timely carried out.
In order to achieve the purpose, the invention provides a myocardial work index monitoring system based on a cardiac shock signal, which is characterized in that: the device comprises a circuit processing module, a signal digital processing module and a signal analysis module;
the circuit processing module is used for carrying out zero phase filtering on a body vibration electric signal caused by heart beat of a human body, removing a direct current signal, retaining an alternating current signal containing vibration information, and amplifying the alternating current signal containing the vibration information by a preset multiple, wherein the body vibration electric signal is micro-vibration generated by change of gravity center of the human body caused by the heart beat;
the signal digital processing module is used for extracting a respiratory component signal in the alternating current signal containing the vibration information by adopting a Butterworth low-pass filter of a front item and a back item, then carrying out differential processing on the amplified alternating current signal containing the vibration information and the respiratory component signal, rejecting the respiratory component in the amplified alternating current signal containing the vibration information, and then reconstructing the alternating current signal without the respiratory component by adopting a wavelet decomposition mode to obtain a BCG signal with noise removed;
the signal analysis module is used for carrying out envelope reconstruction on each cardiac cycle in the BCG signal through a self-adaptive envelope extraction algorithm, determining the position of the highest peak of each envelope, positioning the J peak in each cardiac cycle in a processing window according to the peak time position corresponding to each envelope, then determining the corresponding time of the tops of the H wave, the I wave, the M wave and the N wave of one cardiac cycle according to the position of the J peak, and determining the time gap between the tops of the H wave and the I wave, the time gap between the tops of the I wave and the M wave and the time gap between the tops of the M wave and the N wave;
the signal analysis module is used for substituting the time gap between the H wave and the vertex of the I wave, the time gap between the I wave and the vertex of the M wave, and the time gap between the vertex of the M wave and the vertex of the N wave into a myocardial work index formula, calculating the myocardial work indexes in each cardiac cycle, and carrying out weighted average on all the myocardial work indexes of the processing window to obtain the average myocardial work index at the moment.
The invention monitors the weak change of the body vibration driven by the heart beat in real time, thereby acquiring the heart impact signal with aliasing light path noise and respiration artifacts, then carrying out direct current removal and amplification processing on the signal through a circuit module, and then carrying out mathematical reconstruction on the amplified signal according to a simplified matrix iterative algorithm to realize the purpose of signal denoising. And finally, synchronously acquiring BCG signals, electrocardiosignals, M-shaped ultrasonic curves of a mitral valve and an aortic valve of different human bodies, comparing and verifying to obtain the relation between the BCG waveform and the heart beat stage. The invention firstly proposes to monitor the myocardial performance index in a non-direct contact mode, realizes real-time long-time monitoring, and finally realizes the purpose of monitoring and early warning the heart function through statistical analysis of long-time results.
The research finds that the Ballistocardiogram (BCG) is a signal for recording the mechanical change of blood when the heart beats, and the acquisition mode is simple and does not need to be operated by people. The waveform changes with respect to the force of contraction of the heart, the structure of the heart, the speed of blood flow, the resistance of the peripheral blood vessels, and the filling of the atrioventricular chamber during diastole. The heart attack signal can sensitively and accurately reflect various tiny changes of the heart, so that the heart function can be estimated according to the changes of the heart attack signal. Therefore, compared with the electrocardio signal, the heart impact signal waveform can reflect the change of the heart function earlier. Compared with echocardiography monitoring, the cardiac shock signal can finish household non-inductive monitoring anytime and anywhere.
Therefore, based on the capability of real-time monitoring of the cardiac function by the cardiac shock signal, the invention combines the requirements of practical application, designs a convenient acquisition module, effectively removes interference components in the signal through signal processing, and finally extracts relevant parameters required by calculation of the myocardial performance index through medical verification, thereby not only meeting the requirements of remote medical treatment, but also early warning of the cardiac abnormality in time.
The invention has the beneficial effects that:
the myocardial work index monitoring system based on the cardiac shock signal designed by the invention can meet the requirements of different people on monitoring and early warning of cardiac functions in real time by adopting a non-inductive design. The heart function problem is timely warned before the arrhythmia occurs, the heart diseases are killed in the sprouting stage, the treatment difficulty of the patient is reduced, and the life quality of the prognosis of the patient is greatly improved.
Drawings
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a schematic structural diagram of a ballistocardiographic signal sensing module according to the present invention;
FIG. 3 is a waveform staging result chart of signal verification experiments acquired by the synchronous echocardiogram (UCG) and BCG of the present invention, showing the comparison results of the same person at a moment of rest and movement;
fig. 4 is a distribution diagram for monitoring Tei indices of healthy volunteers and heart failure patients, wherein the abscissa is sample serial numbers of healthy volunteers and heart failure patients, the sample sequence of healthy volunteers is 1, the sample sequence of heart failure patients is 2, and the ordinate is Tei index values of different samples, and the distribution of fig. 4 shows that the Tei index distribution ranges of healthy individuals and heart failure individuals have obvious difference;
fig. 5 is a schematic diagram of a BCG acquisition system mattress format.
The device comprises a heart impact signal sensing module 1, a heart impact signal sensing module 1.1, a first pillow layer, a sponge layer 1.2, an optical fiber sensing layer 1.3, a supporting layer 1.4, a second pillow layer 1.5, a plastic optical fiber 1.6, a single-mode optical fiber 1.7, a mattress surface covering film 1.8, a mattress body 1.9, a circuit processing module 2, a signal digital processing module 3 and a signal analysis module 4.
Detailed Description
The invention is described in further detail below with reference to the following figures and specific examples:
the myocardial work index monitoring system based on the cardiac shock signal as shown in fig. 1 comprises a circuit processing module 2, a signal digital processing module 3 and a signal analysis module 4;
the circuit processing module 2 is used for converting a body vibration optical signal caused by heart pulsation of a human body into a corresponding body vibration electrical signal, performing zero-phase filtering on the body vibration electrical signal, removing a direct current signal, reserving an alternating current signal containing vibration information, and amplifying the alternating current signal containing the vibration information by a preset multiple;
the signal digital processing module 3 is configured to extract a respiratory component signal from an alternating current signal containing vibration information by using a butterworth low-pass filter of a front item and a back item (the front item refers to a first butterworth filtering on an original signal, the back item is a second butterworth filtering performed on a sequence of the first butterworth in a reverse time, a cutoff frequency of the butterworth filter is set to 0.5 Hz), then, the amplified alternating current signal containing the vibration information and the respiratory component signal are subjected to differential processing, a respiratory component in the amplified alternating current signal containing the vibration information is removed, then, the alternating current signal from which the respiratory component is removed is reconstructed by adopting a wavelet decomposition method (by adopting Daubechies (db) series wavelet bases, a decomposition series is set to 10 levels, so that 11 groups of signals of different frequency bands are obtained, signals of frequency bands corresponding to 0.5 to 35hz are reconstructed, signals from which respiratory noise (respiratory noise and circuit noise) are obtained, the heart signal beat artifact is generated, the digital processing module 3 performs denoising on the signals amplified by adopting a complete optical fiber optic signal acquisition and denoising algorithm, thereby, a BCG (BCG) and a complete denoising algorithm which is provided by adopting a complete optical fiber optic fiber sensing algorithm 8978, and a complete denoising method which is extracted, so that the complete denoising algorithm is provided in a complete optical fiber sensing system is extracted, and a complete denoising algorithm (BCG) is extracted;
the module is mainly used for completing the conversion from an optical signal to an electric signal, removing a direct current component in the signal, reserving a useful alternating current component, and then amplifying the signal to amplify the useful component as much as possible;
the signal analysis module 4 is configured to perform envelope reconstruction on each cardiac cycle in the BCG signal through an adaptive envelope extraction algorithm, determine a peak position of each envelope, locate a J peak in each cardiac cycle in a processing window according to a peak time position corresponding to each envelope (the BCG signal is a time-varying signal, the processing window is specified to ensure real-time variation of the adaptive extraction frequency and validity of signal processing, the processing window of the algorithm is set to 8 s), then determine timings corresponding to vertices of an H wave, an I wave, an L wave, an M wave, and an N wave of one cardiac cycle according to the position of the J peak, and determine a time gap between the vertices of the H wave and the I wave, a time gap between the vertices of the I wave and the vertices of the M wave, and a time gap between the vertices of the M wave and the N wave (see, for details, CN113499059a BCG signal processing system and method based on fiber sensing non-contact BCG), where the processing window is set to 8s signals; after the acquired BCG signal is processed according to the signal digital processing module 3, the signal has consistent components, namely H, J, L, N peaks and I, K, M valleys, namely, the peaks of H, I, J, K, L, M and N waves, wherein the H peak represents the start of systole, the mitral valve is closed, the aortic valve is not opened yet, because no external force acts on the aortic valve, the waveform is decreased, the ventricular pressure is increased along with the ventricular pressure, the ventricular pressure is greater than the aortic valve pressure at the I valley, the aortic valve is pushed open to enter rapid ejection, the blood flow is ejected from the ventricle to the aorta to generate a large J peak, the aortic valve is gradually slowed down along with the change of the ventricular pressure to enter a slow ejection period, the blood flow is decreased from the J peak to the K valley, the pressure in the aortic valve is gradually increased, the aortic valve is gradually closed when the pressure in the aortic valve is increased to be consistent with the ventricular pressure, the aortic valve is gradually closed, the L peak is generated at the moment, the aortic valve is closed at the M valley, the blood flow flows into the pulmonary artery and returns to the mitral valve to generate an N peak, and then flows into the ventricle from the vicinity of the mitral valve. Oscillations of blood flow in the ventricle produce back waves of modest amplitude between the N to H waves associated with blood flow reversal during diastole, but there is no specific theory. Therefore, after determining the highest J peak in the cardiac cycle, the peak close to the previous time of the J peak is the H peak, and the trough of the previous time is the I trough, so that the peak time information corresponding to the key wave in each cardiac cycle can be calculated, and the time gap between the H wave and the vertex of the I wave, the time gap between the vertex of the I wave and the vertex of the M wave, and the time gap between the vertex of the M wave and the vertex of the N wave can be determined by the time information of each peak, and the mitral M-mode ultrasound and the M-mode ultrasound of the aortic valve of the echocardiogram are synchronously compared, so that the result shows that in the BCG signal, the top peak of the H wave, i.e., the H wave, corresponds to the beginning of the isovolumetric contraction period, the I wave corresponds to the end of the isovolumetric contraction period, corresponds to the moment when the aortic valve opens, and enters the ejection period of the ventricle, the I wave corresponds to the ejection period of the M wave, and the M wave to the N wave corresponds to the time of isovolumetric relaxation. Therefore, by calculating the time interval between HIs, the isovolumetric contraction time is obtained, calculating the time interval between I waves and M waves, the ejection time is obtained, and calculating the time interval between M waves and N waves, the isovolumetric relaxation time can be obtained.
The signal analysis module 4 is configured to substitute a time gap between the H wave and the I wave vertex, a time gap between the I wave and the M wave vertex, and a time gap between the M wave and the N wave vertex into a myocardial work index formula, calculate a myocardial work index in each cardiac cycle, and perform weighted average on all myocardial work indexes of the processing window according to the formula 2 to obtain an average myocardial work index at the time.
In the above technical method, the signal analysis module 4 substitutes the time gap between the vertices of the H wave and the I wave, the time gap between the vertices of the I wave and the M wave, and the time gap between the vertices of the M wave and the N wave of each cardiac cycle into a myocardial work index calculation formula to calculate the Tei index corresponding to each cardiac cycle.
Tein=(Tict+Tirt)/ET （1）
Wherein, tict represents that isovolumetric systolic time is equivalent to a time gap between the tops of an H wave and an I wave in a cardiac cycle in the BCG signal, tirt represents that isovolumetric diastolic time is equivalent to a time gap between the tops of an M wave and an N wave in a cardiac cycle in the BCG signal, ET represents that ejection time is equivalent to a time gap between the tops of an I wave and an M wave, and the average of the Tei indexes of each cycle in a signal processing window is obtained as a real-time Tei index.
The BCG signal waveform is proved to have positive correlation with the heart beating period through research and experiments, and the relationship between the waveform and the heart beating period is obtained through proving, and the specific method is as follows:
step A: 50 healthy volunteers with the ages of 20 to 60 are recruited, and the volunteers select the standard that a professional doctor diagnoses the normal electrocardiogram and ultrasonically displays the normal heart structure without hypertension, severe respiratory diseases and Parkinson disease;
and B: calling 50 patients with heart diseases, wherein the data of the patients are used as comparative data, and the volunteer enrollment criteria of the patients are that cardiovascular doctors diagnose the condition of heart function weakening (the symptoms are different, and random selection is adopted);
and C: b, collecting BCG signals, electrocardiosignals of four limbs of the volunteers and synchronous electrocardiosignals of M-type ultrasound and ultrasound of the mitral valve and the aortic valve of the left ventricle by adopting the BCG collecting system synchronous electrocardiogram and multi-thread ultrasound equipment for the volunteers recruited in the step A and the step B;
step D: before signals are collected, the BCG collection equipment is synchronized with the medical electrocardio equipment, the synchronization method adopts software synchronization, the collection upper computer of the BCG equipment and the collection upper computer of the electrocardio equipment are arranged in the same host to ensure that clocks read by the two upper computers are completely consistent, then a module for capturing external actions is added into the upper computer of the BCG equipment, the module has the function of capturing the actions of starting the electrocardio equipment to record the electrocardio data in real time, once the electrocardio equipment is started to record the electrocardio data, the capture module of the BCG equipment acquires the actions and records the collection time (accurate to millisecond), and the BCG data is recorded at the same time, so that the synchronization of the BCG equipment and the medical electrocardio equipment is completed; the cardiac ultrasonic acquisition equipment is an ultrasonic machine with electrocardio equipment, and the electrocardio data of the equipment are synchronized;
step E: after each volunteer is kept in a calm state for 15 minutes, the volunteer lies on a bed, the head and the shoulders are arranged on the monitoring pillow, two hands are kept on two sides of the body, the volunteer is kept in a calm state, the chest is exposed outside, the heart color ultrasound is conveniently carried out at the back, the four limbs are pasted with electrocardio electrode plates, after the preparation, an electrocardio device and BCG acquisition equipment are started, then the heart color ultrasound is operated by a professional doctor to respectively find a mitral valve and an aorta, after an ultrasonic signal is stabilized, an operator holds an ultrasonic probe to keep still, and the M-shaped ultrasonic curves of the mitral valve and the aortic valve and ultrasonic electrocardio data are recorded;
step F: comparing 100 acquired synchronous signals, firstly adjusting the data acquisition rate of BCG synchronous medical electrocardiogram equipment and ultrasonic self-contained electrocardiogram acquisition to be 100Hz, finding out time information Q1 of the same cardiac cycle in data acquired by two sets of electrocardiogram equipment, finding out BCG data corresponding to Q time electrocardiogram from BCG synchronous electrocardiogram data, finding out M-type ultrasonic influence data corresponding to Q time electrocardiogram from data acquired by an ultrasonic self-contained electrocardiogram module, and finally calibrating and synchronizing the occurrence time of the BCG data and the M-type ultrasonic data by ECG data to realize synchronous comparison and analysis of the BCG, ECG and UCG data;
g: finding the occurrence time in the ultrasound corresponding to the ECG electrocardiosignal occurrence time determined in the step E, and obtaining M-shaped ultrasound curves of the mitral valve and the aortic valve corresponding to the occurrence time;
step H: the data of 100 volunteers are compared to obtain a comparison waveform shown in fig. 3, fig. 3 shows the healthy volunteer data of one heart rate 58, 7 patients cannot acquire clear ultrasonic data by adopting a lying posture, so the 7 data are removed, and other 93 data all show the comparison result of fig. 3, wherein the time gap between the vertexes of the H wave and the I wave, the time gap between the vertexes of the I wave and the M wave, the time gap between the vertexes of the M wave and the N wave, the ultrasonic display and the isovolumetric contraction period, ejection period and isovolumetric relaxation period errors obtained by the same operator are all less than 0.005.
It was therefore concluded that: the time gap between the vertexes of the H wave and the I wave corresponds to the isovolumetric contraction period of the heart pumping blood, the time gap between the vertexes of the I wave and the M wave corresponds to the ejection period of the heart pumping blood, the time gap between the vertexes of the M wave and the N wave corresponds to the isovolumetric diastole period of the heart pumping blood, and the time gap between the N wave and the next vertex of the H wave corresponds to the fast filling period and the slow filling period of the diastole period of the heart pumping blood.
In the technical method, the heart impact signal sensing module 1 is further arranged, and the heart impact signal sensing module 1 is used for collecting body vibration signals caused by heart pulsation of a human body through an optical fiber macrobend loss testing method. The heart attack signal sensing module 1 is a sensing module which can monitor the micro-vibration signal (BCG signal) of the body gravity center change caused by the heart beat and can meet the requirement of BCG signal acquisition.
In the technical method, the device further comprises a heart impact signal sensing module 1, wherein the heart impact signal sensing module 1 is used for collecting body vibration optical signals caused by the heart pulsation of a human body.
Among the above-mentioned technical method, heart impact signal sensing module 1 designs the form for the pillow, as fig. 2, make things convenient for real-time supervision at home, its packaging structure who adopts 5 layers, each layer material can be fixed in the adhesion all around from the top down, be first pillow layer 1.1, sponge layer 1.2, optical fiber sensing layer 1.3, supporting layer 1.4 and second pillow layer 1.5 respectively, sponge layer 1.2 is used for keeping apart first pillow layer 1.1 protection optical fiber sensing layer 1.3, supporting layer 1.4 is used for providing the hard support bottom to optical fiber sensing layer 1.3 and supports the sensitivity of sensing, first pillow layer 1.1 and second pillow layer 1.5 are used for increasing the travelling comfort when heart impact signal sensing module 1 gathers the heart impact signal. The first pillow layer 1.1 and the second pillow layer 1.5 are latex layers or cotton layers, and the supporting layer 1.4 is a PVC material layer.
In the technical method, the optical fiber sensing layer adopts a single-mode optical fiber to coil 8 sensing monitoring points in a sensing area, each sensing monitoring point is a concentric circle monitoring area wound by 1.7 of the single-mode optical fiber, the radius is 1-3cm, the coiling radius of each concentric circle sensing area is equal in principle, the error is less than 0.1cm, and in order to ensure the sensing performance, two plastic optical fibers 1.6 with the length being 2.5 times of the radius length are respectively fixed in each concentric circle monitoring area in a crossed mode by taking the circle center as an intersection point. The design is to adjust the loss rate of the optical fiber after being pressed, and control the loss rate in a certain range, so as to sense the vibration generated by the body driven by the heart beat, and the vibration can change the light intensity loss of the optical fiber in the pressed sensing area, and acquire the heart beat signal by using the change of the light intensity. Due to the adoption of the design of the monitoring pillow, the interference of respiration on the heart impact signal is reduced in the acquisition, and the subsequent signal processing difficulty is reduced. Fig. 5 is a mattress form of BCG signal acquisition, the single mode fiber 1.7 is located between the mattress surface covering film 1.8 and the mattress body 1.9, the sensing layer is thin and elastic, and therefore can be rolled up and stored at will, the elastic structure design makes the acquisition process very comfortable and not to attract attention, and the form can also be placed at any part of the body to acquire the micro-vibration BCG signal caused by the change of the body gravity center caused by the heart beat.
In the above technical method, the signal digital processing module 3 ensures that each processing window at least contains an expiratory signal and an inspiratory signal when processing 10 seconds of heartbeat data each time, extracts the BCG signal of each cardiac cycle in the expiratory signal, and takes the average of the amplitudes of the extracted BCG signals of a single cardiac cycle as a template of a correction signal, and corrects the BCG signals of other moments in the processing window in a corresponding proportion by using the template.
After signal denoising, the important point of the present invention is different from other algorithms in that the signal is modified according to the evidence that the BCG signal is found in the stark study during inspiration and expiration, see the references Isaac star C K f. On the house of the respiratory variation of the balllistocardiogram, with a Note On right heart Arrhythmia [ J ]. J Clin Invest, 1946,1 (25): 53-64, the contribution of the left and right ventricles to the BCG signal is not uniform, the experiment verifies that the left and right ventricles contribute substantially to the BCG signal at expiration, inspiration is dominated by the right ventricle contribution of the BCG signal, the Tei index calculated subsequently in the present invention mainly considers the myocardial work index of the left ventricle, so we have each data processing frame length of 8 seconds, ensure that each data processing frame length contains one cycle (i.e. contains respiration and inspiration process), then extracts each cycle of the BCG signal, and takes the average of the signal period as the template for modifying the other frames at the subsequent time.
In the above technical method, the signal analysis module 4 is configured to extract each cardiac cycle of the modified BCG signal, locate a time gap between the vertices of the H wave and the I wave, a time gap between the vertices of the I wave and the M wave, and a time gap between the vertices of the M wave and the N wave of each cardiac cycle, and bring the time gap between the vertices of the H wave and the I wave, the time gap between the vertices of the I wave and the M wave, and the time gap between the vertices of the M wave and the N wave of each cardiac cycle into a myocardial performance index calculation formula Tei = (Tict + Tirt)/ET;
wherein, tict represents that the isovolumetric systolic time is equivalent to the time gap between the H wave and the I wave vertex in the BCG signal, tirt represents that the isovolumetric diastolic time is equivalent to the time gap between the M wave and the N wave vertex in the BCG signal, ET represents that the ejection time is equivalent to the time gap between the I wave and the M wave vertex, and the average of the Tei indexes of each period in the signal processing window is calculated to be used as the real-time Tei index.
The signal analysis module 4 extracts the calculation parameters required by the myocardial performance index, researches and experiments prove that BCG signal waveforms have positive correlation with the heart beating period, the relationship between the waveforms and the heart beating period is obtained through the proof, the corresponding isovolumetric contraction period and isovolumetric diastole period in the BCG waveforms are accurately found by using the proof result, and the myocardial performance index is calculated through the existing formula;
the BCG signal processed by the signal digital processing module 3 has good stability, and firstly, the amplitude change of the signal is not influenced by respiration and human body weight and is only related to the signal vibration strength; second, the signal has uniform components, with each cardiac cycle having several components, H, I, J, K, L, M and N.
This embodiment has designed a convenient intelligent optical fiber monitoring pillow, and the pillow body is uncharged, and safe radiationless, the pillow adopts 5 layers of sandwich structure encapsulation, provides comfortable sleep condition when guaranteeing signal normal collection. The acquired aliasing signals are subjected to photoelectric conversion processing by the circuit module, then the processed signals are sent to the digital signal processing module for denoising and repairing, finally the BCG signals are subjected to feature extraction by the signal analysis module, medical parameters related to cardiac function monitoring are extracted, and the myocardial performance index is calculated according to a medical myocardial performance index calculation formula. The invention breaks through the traditional detection mode of the myocardial performance index for the first time, the traditional detection method is limited by time, place and operators, and the invention provides a new method for the remote household real-time monitoring of the cardiac function.
A myocardial work index monitoring method based on a cardiac shock signal comprises the following steps:
step 1: the circuit processing module 2 converts a body vibration optical signal caused by the heart beat of a human body into a corresponding body vibration electric signal, performs zero-phase filtering on the body vibration electric signal, removes a direct current signal, retains an alternating current signal containing vibration information, and amplifies the alternating current signal containing the vibration information by a preset multiple;
when the heart beats, the body can be driven to vibrate up, down, left and right, the vibration brings displacement change of the body conscious, so that light intensity transmitted in the optical fiber is changed, a vibration signal is sensed by extracting a change value of the light intensity, and for convenience of signal processing, an optical signal carrying the vibration signal is converted into a micro-electrical signal through a photoelectric converter;
and 2, step: the signal digital processing module 3 extracts a respiratory component signal in the alternating current signal containing the vibration information by adopting a butterworth low-pass filter of the preceding item and the following item, then performs differential processing on the amplified alternating current signal containing the vibration information and the respiratory component signal to remove the respiratory component in the amplified alternating current signal containing the vibration information, and then reconstructs the alternating current signal without the respiratory component in a wavelet decomposition mode to obtain a noise-filtered BCG signal (the signal is a signal generated by the heart beat);
and step 3: the signal analysis module 4 carries out envelope reconstruction on each cardiac cycle in the BCG signal through a self-adaptive envelope extraction algorithm, determines the position of the highest peak of each envelope, positions the J peak in each cardiac cycle in a processing window according to the peak time position corresponding to each envelope, then determines the corresponding time of the tops of the H wave, the I wave, the M wave and the N wave of one cardiac cycle according to the position of the J peak, and determines the time gap between the tops of the H wave and the I wave, the time gap between the tops of the I wave and the M wave and the time gap between the tops of the M wave and the N wave;
the research result shows that each electrocardiosignal corresponds to a BCG signal, the electrocardiosignals occur before the BCG signal, statistical analysis finds that the RR interval time (R is the highest peak in the ECG signal and the time of one cardiac cycle) calculated by the electrocardiosignals is consistent with the JJ interval time calculated by the BCG signal in height, the average absolute error of 18 healthy subjects is 4ms, the regression linearity reaches 0.962, the H wave vertex time of the BCG signal is always close to the R wave vertex time of the ECG, the average absolute error of two parameters of the 18 subjects is 8ms, meanwhile, the corresponding relation still exists when the subjects have atrial premature beat, the phenomenon accords with the electro-mechanical Oldham principle, and therefore the BCG signal is highly related to the mechanical beat of the heart, and the R wave of the ECG corresponds to the beginning of the systolic period of the heart, namely the H wave also corresponds to the beginning of the systolic period. Based on the corresponding relation, the ECG, BCG, M-mode ultrasound of a mitral valve and an aortic valve are synchronously recorded, pressure curves of a left ventricle, an outside of the aortic valve and a right ventricle in a heart cavity are recorded through an interventional catheter technology, monitoring results of 10 groups of different individuals show that the vertex time of an H wave corresponds to the starting time of the mitral valve, the vertex time of an I wave corresponds to the opening time of the aortic valve, the time gap between the vertexes of an HI wave corresponds to the constant volume contraction time, the time length from the I wave to the M wave corresponds to the blood ejection period time length, wherein a fast blood ejection period and a slow blood ejection period are included, the vertex time of the M wave corresponds to the closing time of the aortic valve, the vertex time of an N wave corresponds to the vicinity of the opening time of the mitral valve, therefore, the time gap between the vertexes of an MN wave corresponds to the constant volume relaxation time length, and the vertex of the H wave from the vertex of the N wave to the next cardiac cycle corresponds to the fast filling time length and the slow filling time length of the relaxation period. Finally, the ultrasonic and BCG signals are synchronously recorded, and the statistical average absolute error of the time parameters monitored by the BCG signals is within 10ms, so that the research result conclusion shows that: the time gap between the vertexes of the H wave and the I wave of the BCG waveform is equivalent to the isovolumetric systolic time of the heart beat, the time gap between the vertexes of the I wave and the M wave is equivalent to the ejection period time of the heart beat, and the time gap between the vertexes of the M wave and the N wave is equivalent to the isovolumetric diastolic time of the heart beat;
and 4, step 4: the signal analysis module 4 substitutes the time gap between the H wave and the I wave vertex, the time gap between the I wave and the M wave vertex, and the time gap between the M wave and the N wave vertex into a myocardial work index formula, calculates the myocardial work index in each cardiac cycle, and performs weighted average on all the myocardial work indexes of the processing window to obtain the average myocardial work index at the moment, which is used to ensure the accuracy of the data of each frame. The signal analysis module 4 draws scattered points which change along with time according to the average myocardial work index calculated for a long time, and statistically analyzes the myocardial function of the tested person. Thereby achieving the purpose of early warning the heart function.
Details not described in this specification are within the skill of the art that are well known to those skilled in the art.
Claims (8)
1. A myocardial work index monitoring system based on a cardiac shock signal is characterized in that: the device comprises a circuit processing module (2), a signal digital processing module (3) and a signal analysis module (4);
the circuit processing module (2) is used for carrying out zero-phase filtering on body vibration electric signals caused by heart pulsation of a human body, removing direct current signals, retaining alternating current signals containing vibration information and amplifying the alternating current signals containing the vibration information by preset times;
the signal digital processing module (3) is used for extracting a respiratory component signal in the alternating current signal containing the vibration information by adopting a Butterworth low-pass filter of a front item and a back item, then carrying out differential processing on the amplified alternating current signal containing the vibration information and the respiratory component signal, rejecting the respiratory component in the amplified alternating current signal containing the vibration information, and then reconstructing the alternating current signal without the respiratory component by adopting a wavelet decomposition mode to obtain a BCG signal with noise removed;
the signal analysis module (4) is used for carrying out envelope reconstruction on each cardiac cycle in the BCG signal through a self-adaptive envelope extraction algorithm, determining the highest peak position of each envelope, positioning a J peak in each cardiac cycle in a processing window according to the peak time position corresponding to each envelope, then determining the corresponding time of the top points of the H wave, the I wave, the M wave and the N wave of one cardiac cycle according to the position of the J peak, and determining the time gap between the top points of the H wave and the I wave, the time gap between the top points of the I wave and the M wave and the time gap between the top points of the M wave and the N wave;
the signal analysis module (4) is used for substituting a time gap between the vertexes of the H wave and the I wave, a time gap between the vertexes of the I wave and the M wave, and a time gap between the vertexes of the M wave and the N wave into a myocardial work index formula, calculating the myocardial work indexes in each cardiac cycle, and carrying out weighted average on all the myocardial work indexes of the processing window to obtain an average myocardial work index at the moment;
the heart shock signal sensing module (1) is used for collecting body vibration optical signals caused by heart pulsation of a human body by an optical fiber macrobend loss testing method;
the signal analysis module (4) brings a time gap between the vertexes of the H wave and the I wave, a time gap between the vertexes of the I wave and the M wave, and a time gap between the vertexes of the M wave and the N wave of each cardiac cycle into a myocardial work index calculation formula Tei = (Tict + Tirt)/ET;
wherein, tict represents that the isovolumetric systolic time is equivalent to the time gap between the H wave and the I wave vertex in the BCG signal, tirt represents that the isovolumetric diastolic time is equivalent to the time gap between the M wave and the N wave vertex in the BCG signal, ET represents that the ejection time is equivalent to the time gap between the I wave and the M wave vertex, and the average of the Tei indexes of each period in the signal processing window is calculated to be used as the real-time Tei index.
2. The system of claim 1, wherein the cardiac activity index monitoring system based on the ballistocardiogram signal comprises: the circuit processing module (2) is used for converting a body vibration optical signal caused by the heart beat of a human body into a corresponding body vibration electrical signal.
3. The system of claim 1, wherein the cardiac activity index monitoring system based on the ballistocardiogram signal comprises: the heart strikes packaging structure that signal sensing module (1) adopted 5 layers, from the top down, be first pillow layer (1.1) respectively, sponge layer (1.2), optic fibre sensing layer (1.3), supporting layer (1.4) and second pillow layer (1.5), sponge layer (1.2) are used for keeping apart first pillow layer (1.1) protection optic fibre sensing layer (1.3), supporting layer (1.4) are used for providing the hard support end support to optic fibre sensing layer (1.3), first pillow layer (1.1) and second pillow layer (1.5) are used for increasing the travelling comfort when heart strikes signal sensing module (1) and gathers heart impact signal.
4. The system of claim 3, wherein the cardiac activity index is based on the ballistocardiogram signal, and the system further comprises: the first pillow layer (1.1) and the second pillow layer (1.5) are latex layers or cotton layers, and the supporting layer (1.4) is a PVC material layer.
5. The system of claim 3, wherein the cardiac activity index monitoring system based on the ballistocardiogram signal comprises: the optical fiber sensing layer (1.3) is characterized in that a single-mode optical fiber (1.7) is coiled on N sensing monitoring points in a sensing area, each sensing monitoring point is a concentric circle monitoring area wound by the single-mode optical fiber (1.7), and two plastic optical fibers (1.6) are respectively fixed in a crossed mode in each concentric circle monitoring area by taking the circle center as an intersection point.
6. The system of claim 1, wherein the cardiac activity index monitoring system based on the ballistocardiogram signal comprises: and the signal digital processing module (3) ensures that each processing window at least comprises an expiration signal and an inspiration signal when processing the heartbeat data of K seconds each time, extracts the BCG signal of each cardiac cycle in the expiration signal, takes the average of the amplitude values of the extracted BCG signal of a single cardiac cycle as a template of a correction signal, and corrects the BCG signals of other moments in the processing window by using the template in a corresponding proportion.
7. The system of claim 6, wherein the cardiac activity index monitoring system is based on a cardiac shock signal, and comprises: the signal analysis module (4) is used for extracting each cardiac cycle of the corrected BCG signal, positioning a time gap between the vertexes of the H wave and the I wave, a time gap between the vertexes of the I wave and the M wave, and a time gap between the vertexes of the M wave and the N wave of each cardiac cycle, and substituting the time gap between the vertexes of the H wave and the I wave, the time gap between the vertexes of the I wave and the M wave, and the time gap between the vertexes of the M wave and the N wave of each cardiac cycle into a myocardial work index calculation formula Tei = (Tict + Tirt)/ET;
wherein, tict represents that the isovolumetric systolic time is equivalent to the time gap between the H wave and the I wave vertex in the BCG signal, tirt represents that the isovolumetric diastolic time is equivalent to the time gap between the M wave and the N wave vertex in the BCG signal, ET represents that the ejection time is equivalent to the time gap between the I wave and the M wave vertex, and the average of the Tei indexes of each period in the signal processing window is calculated to be used as the real-time Tei index.
8. A myocardial performance index monitoring method based on ballistocardiogram signals using the system of claim 1, comprising the steps of:
step 1: the circuit processing module (2) is used for carrying out zero-phase filtering on body vibration electric signals caused by heart pulsation of a human body, removing direct current signals, retaining alternating current signals containing vibration information and amplifying the alternating current signals containing the vibration information by preset times;
step 2: the signal digital processing module (3) adopts a Butterworth low-pass filter of a previous item and a later item to extract respiratory component signals in the alternating current signals containing the vibration information, then performs differential processing on the amplified alternating current signals containing the vibration information and the respiratory component signals to remove respiratory components in the amplified alternating current signals containing the vibration information, and then reconstructs the alternating current signals from which the respiratory components are removed in a wavelet decomposition mode to obtain BCG signals from which noise is removed;
and step 3: the signal analysis module (4) carries out envelope reconstruction on each cardiac cycle in the BCG signal through a self-adaptive envelope extraction algorithm, determines the position of the highest peak of each envelope, positions the J peak in each cardiac cycle in a processing window according to the peak moment position corresponding to each envelope, then determines the corresponding moments of the tops of the H wave, the I wave, the M wave and the N wave of one cardiac cycle according to the position of the J peak, and determines the time gap between the tops of the H wave and the I wave, the time gap between the tops of the I wave and the M wave and the time gap between the tops of the M wave and the N wave;
and 4, step 4: the signal analysis module (4) substitutes the time gap between the H wave and the peak of the I wave, the time gap between the I wave and the peak of the M wave, and the time gap between the M wave and the peak of the N wave into a myocardial work index formula, calculates the myocardial work index in each cardiac cycle, and weights and averages all the myocardial work indexes of the processing window to obtain the average myocardial work index at the moment.
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