CN114515151A - Electrocardiosignal acquisition system and processing method based on artificial intelligence - Google Patents
Electrocardiosignal acquisition system and processing method based on artificial intelligence Download PDFInfo
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Abstract
The invention discloses an artificial intelligence-based electrocardiosignal acquisition system and a processing method, and the system comprises a physiological signal acquisition module, a wireless transmission module and an intelligent terminal module, wherein the physiological signal acquisition module is used for acquiring various physiological signals of a serious patient; the wireless transmission module performs wireless transmission after A/D conversion on the analog signals acquired by the acquisition module; the intelligent terminal processes and analyzes the received digital signals, evaluates the physiological condition of the serious sick and wounded, realizes wireless continuous monitoring of the serious sick and wounded, and automatically alarms abnormal indexes. The electrocardiosignal acquisition system has simple equipment structure, can be worn quickly and used for monitoring patients continuously, can be used for de-noising acquired signals, extracting and analyzing signal characteristics of high-quality signal screening through the internal program setting of the system, accurately judges origin parts of arrhythmia causes of the patients and provides medical advice and treatment schemes in time.
Description
Technical Field
The invention belongs to the technical field of signal processing, and particularly relates to an artificial intelligence-based electrocardiosignal acquisition system and a processing method.
Background
The electrical excitation from the sinoatrial node of the heart at each period is transmitted to the atrium and the ventricle in turn according to a certain path and time course, so as to trigger the excitation of the whole heart, so that the heart contracts periodically, thereby promoting the blood circulation in the whole body. In each cardiac cycle, the time, the way and the sequence of the electrical changes occurring in the excitation process of the various parts of the heart have certain rules. The measuring electrode is placed at a certain position on the surface of a human body, and the recorded heart electrical change curve is the ECG (electrocardiogram), so that a plurality of heart physiological conditions can be obtained through electrocardiosignals. Meanwhile, in emergency medical treatment, all instruments need to work cooperatively to monitor vital signs of patients in real time so as to quickly and accurately obtain all data, most of the existing processing modes are that the instruments such as various blood pressure instruments, heart rate measuring instruments, oximeters, electrocardiograph monitors and the like monitor the vital signs respectively, and then medical workers record and analyze whether the data are abnormal or not so as to draw a conclusion on the life state of the patients.
Disclosure of Invention
In order to solve the problems, the invention designs an artificial intelligence-based electrocardiosignal acquisition system and a processing method, the equipment has a simple structure, the system can be worn quickly and used for continuously monitoring patients, the acquired signals are subjected to noise elimination and high-quality signal screening and signal characteristic extraction and analysis through the internal program setting of the system, the origin part of the arrhythmia etiology of the patients is accurately judged, and a medical suggestion and a treatment scheme are given in time.
In order to realize the purpose, the invention adopts the following technical scheme: electrocardiosignal collection system based on artificial intelligence, including physiological signal collection module, wireless transmission module and intelligent terminal module, signal collection module includes electrocardiosignal collection module, its characterized in that: the physiological signal acquisition module acquires various physiological signals of the critically ill patients; the wireless transmission module performs wireless transmission after A/D conversion on the analog signals acquired by the acquisition module; the intelligent terminal processes and analyzes the received digital signals, evaluates the physiological condition of the serious sick and wounded, realizes wireless continuous monitoring of the serious sick and wounded, and automatically alarms abnormal indexes.
The electrocardiosignal acquisition system based on artificial intelligence is further improved as follows: the signal acquisition module comprises wearable equipment and a sensor arranged on the wearable equipment, the wearable equipment comprises a plurality of elastic mounting belts which are arranged in a mutual lead mode according to the structure of a human body, and the elastic mounting belts are movably spliced; the wireless transmission module adopts an anti-interference technology based on frequency agility to avoid signal interference.
The electrocardiosignal acquisition system based on artificial intelligence is further improved as follows: the signal acquisition module also comprises a pulse, blood oxygen and electroencephalogram data acquisition module arranged on the head and a myoelectricity, cardiopulmonary sound, electrocardio and body temperature data monitoring module arranged at the corresponding position on the body.
The electrocardiosignal acquisition system based on artificial intelligence is further improved as follows: the intelligent terminal module comprises an intelligent control terminal and a central control system, the intelligent control terminal is matched with the physiological signal acquisition module through a wireless transmission module in a network mode, the physiological signal acquisition module transmits acquired data information to the central control system through wireless transmission to be stored and analyzed, the central control system feeds back the processed information to the intelligent control terminal, the intelligent control terminal is provided with an alarm module, the central control system sets threshold values for parameters such as electrocardio, pulse, blood oxygen and body temperature, and the central control system compares the processed parameters with the threshold values and feeds back the processed parameters to the alarm module of the intelligent control terminal to achieve abnormal parameter alarm.
In order to realize the purpose, the invention adopts the following technical scheme: the signal processing method of the electrocardiosignal acquisition system comprises the following steps:
1) an ECG signal acquisition module acquires electrocardiosignals;
2) preprocessing of the ECG signal;
analyzing and denoising the ECG signal by adopting a wavelet transform method;
processing the low-scale wavelet coefficients d1 and d2 by a soft threshold value method, and eliminating high-frequency noise by a large-amplitude attenuation coefficient method;
processing the scale 3 wavelet coefficient containing the important input signal by adopting a soft threshold value and hard threshold value compromise algorithm;
2) judging the quality of the ECG signal, and screening signals with good quality;
screening a signal with good quality according to characteristics including but not limited to a threshold value and a peak part sample number; 3) electrocardiosignal feature extraction
The detection of the singular points of the ECG signal is finished based on a biorthogonal quadratic B-spline wavelet transform method; and the positioning precision of the R wave is improved by adopting a dynamic valve value method. Analyzing the wavelet coefficient D4, extracting an extreme value pair, obtaining the extreme value pair for accurately positioning the R wave by adopting threshold processing and filtering, restoring the position of the extreme value pair to the reconstructed ECG signal, and finding the maximum value in the maximum value interval to obtain R wave positioning; after the R wave is positioned, Q, S waves can be positioned by searching a minimum value point near the R wave according to the position of the R wave;
The length of a detection interval is controlled by taking the positions of two adjacent R waves as a marker post and the mean value of an RR interval, so that two heart beats between the two adjacent R waves, respective T waves and P waves are accurately detected; measuring respective detection areas of the T wave and the P wave to finish accurate positioning of P, Q, R, S, T waves of the ECG signal;
4) signal analysis
Dividing the data obtained in the step 3) into two first-level subclasses which are indoor blocks in excitation origin abnormality and excitation conduction abnormality respectively and are used for training a support vector machine (SVM 1);
classifying the excitation origin abnormality into sinus rhythm and ectopic rhythm respectively for training a support vector machine (SVM 2);
dividing the ectopic heart rate into an active ectopic heart rate and a passive ectopic heart rate for training the SVM 3; dividing the active ectopic heart rhythm into two categories, namely supraventricular premature beat and ventricular premature beat, and using the two categories to train the SVM 4;
passive ectopic rhythms are classified into two categories, supraventricular and ventricular escapes, respectively, for training SVM 5.
The electrocardiosignal acquisition system based on artificial intelligence is further improved as follows: the electrocardiosignals are divided into seven classes by five support vector machines, so that the heart condition of the sick and wounded is monitored in real time.
According to the invention, the movable mounting belt provided with the signal acquisition sensor is used for collecting, storing, analyzing and feeding back electrocardiosignals of a human body and the like in real time, and medical personnel quickly receive a feedback result, so that the treatment time is saved, the medical personnel can quickly respond to help, and the treatment rate of the sick and wounded is finally improved. The movably spliced mounting belt is convenient for patients to wear at the first time to monitor vital signs of the patients, the central control system carries out preset program processing on collected electrocardiosignals, carries out noise elimination and low-quality signal screening on the electrocardiosignals, carries out electrocardiosignal feature extraction according to high-quality signals, analyzes and compares the signals, accurately judges arrhythmia symptoms, quickly traces origin parts of arrhythmia causes, and provides timely medical suggestions and treatment schemes.
Detailed Description
The present invention is further described with reference to the following drawings and specific examples, which are provided for the purpose of explaining the technical solutions of the present invention in detail.
The electrocardiosignal acquisition system based on artificial intelligence comprises a physiological signal acquisition module, a wireless transmission module and an intelligent terminal module, wherein the signal acquisition module comprises an electrocardiosignal acquisition module which realizes acquisition of various physiological signals of a serious patient; the wireless transmission module performs wireless transmission after A/D conversion on the analog signals acquired by the acquisition module; the intelligent terminal processes and analyzes the received digital signals, evaluates the physiological condition of the serious sick and wounded, realizes wireless continuous monitoring of the serious sick and wounded, and automatically alarms abnormal indexes. Specifically, the signal acquisition module comprises a wearable device and a sensor arranged on the wearable device, the wearable device comprises a plurality of elastic mounting belts which are arranged according to the structure of a human body and are mutually connected, the elastic mounting belts are movably spliced and comprise elastic mounting belts arranged on the head and elastic mounting belts positioned on the body, the head mounting belts are mainly used for acquiring data such as pulse, blood oxygen, electroencephalogram and the like, the elastic mounting belts of the body are mainly used for acquiring data such as myoelectricity, cardiopulmonary sounds, electrocardio, body temperature data and the like, the elastic mounting belts are mutually connected in a circuit way and are movably spliced and connected, and the wearable device is worn on a patient through a movable splicing structure such as a hasp or a magic tape; the wireless transmission module adopts an anti-interference technology based on frequency agility to avoid signal interference, and specifically adopts ZigBee/Bluetooth to wirelessly transmit physiological signals to the data intelligent terminal module.
The intelligent terminal module comprises an intelligent control terminal and a central control system, the intelligent control terminal is in network adaptation with the physiological signal acquisition module through the wireless transmission module, the physiological signal acquisition module transmits acquired data information to the central control system through wireless transmission for data storage and analysis, the central control system feeds back processed information to the intelligent control terminal, the intelligent control terminal is provided with an alarm module, the central control system sets threshold values for parameters such as electrocardio, pulse, blood oxygen and body temperature, and the central control system compares the processed parameters with the threshold values and feeds back the processed parameters to the intelligent control terminal alarm module to realize abnormal parameter alarm. For example, the central control system sets the blood oxygen saturation threshold value to be 90%, when the parameter information acquired by the blood oxygen signal acquisition module is subjected to data processing, the data is compared with the set threshold value, and when the blood oxygen saturation is lower than 90%, the central control system feeds back the comparison result to the intelligent control terminal and triggers the alarm module to alarm; setting the minimum diastolic blood pressure threshold Bm to be 60, the maximum diastolic blood pressure threshold Bx to be 90, the minimum systolic blood pressure threshold Dm to be 90, the maximum systolic blood pressure threshold Dx to be 140, and triggering an alarm after the acquired data are processed, compared and analyzed with the thresholds and abnormal data results are obtained.
The electrocardiosignal acquisition system can automatically screen and analyze the acquired electrocardiosignals, particularly can filter the acquired signals, screen out ECG signal noise, judge the quality of the electrocardiosignals, extract the electrocardiosignal characteristics by using the screened high-quality electrocardiosignals, display the electrocardiosignals on a display in a graphic form, accurately judge arrhythmia symptoms by using the data, quickly trace origin parts of arrhythmia causes and provide timely medical suggestions and treatment schemes. The signal processing method of the electrocardiosignal acquisition system comprises the following steps: 1) an ECG signal acquisition module acquires electrocardiosignals;
2) preprocessing of the ECG signal;
after the original electrocardiosignal data is obtained, the original electrocardiosignal data needs to be preprocessed to remove noise contained in the electrocardiosignal. In the invention, a wavelet transform method is adopted to analyze and denoise signals. A biorthogonal quadratic B-spline wavelet is selected, which is a first-order smoothing function. The ECG signal noise mainly comprises baseline drift, power frequency interference and electromyographic noise, and the frequency band distribution of each noise signal contained in the ECG signal is also different: the baseline drift is 0-0.5Hz, and the power frequency interference is 50-60 Hz; myoelectric noise is 50-2 KHz, the range is wider, and the myoelectric noise is similar to Gaussian sound. Furthermore, the center frequency of the QRS wave group, the main component of the ECG signal, is about 17 Hz. The QRS wave group energy in the invention is mainly distributed on the scales of 3 and 4 after wavelet transformation; the power frequency interference energy is mainly distributed on 2 scales after wavelet transformation; the electromyographic noise energy is mainly distributed on the scales of 1, 2 and 3 after wavelet transformation. The filtering method adopted by the method comprises the following steps: and processing the low-scale wavelet coefficients d1 and d2 by a soft threshold value method, and eliminating high-frequency noise by a large-amplitude attenuation coefficient method. Aiming at the scale 3 wavelet coefficient containing important input signals, the invention adopts an algorithm of soft and hard threshold compromise, not only eliminates the electromyographic noise, but also retains the original input signals as far as possible, and does not influence the accurate extraction of the subsequent electrocardio characteristic parameters. At the same time, baseline wander is suppressed by the all-pass down-pass filter.
2) Judging the quality of the ECG signal, and screening signals with good quality;
screening a signal with good quality according to characteristics including but not limited to a threshold value and a peak part sample number; the evaluation standard is as follows:
a1: the threshold is greater than 40% by the 3mv portion, which is considered to meet the criterion.
A2: the criterion is satisfied when the first derivative is greater than 0.3 (i.e., the number of peak portion samples) is greater than 40%.
A3-greater than 80% of the lead fall-off, this criterion is met.
A4: the portion satisfying the determination conditions in a1, a2, and A3 is a potential failure point, and the criterion is satisfied when the potential failure portion is larger than 68.5%.
Through the four judgment standards, signals with poor contact, lead falling and poor quality can be screened out without processing, and signals with good quality can be used for disease analysis.
3) Electrocardiosignal feature extraction
And (3) finishing the detection of the singular points of the ECG signal by adopting a method based on biorthogonal quadratic B-spline wavelet transform. The ECG signal R wave presents an extreme value pair form of the maximum amplitude value under the scale 4, the frequency band range (11.3-22.5 Hz) of the scale 4 is closest to the QRS wave central frequency (17 Hz), and the influence of unfiltered and clean noise outside the bandwidth is effectively avoided, so that the detection precision of the R wave is ensured. And the positioning precision of the R wave is improved by adopting a dynamic valve value method. And analyzing the wavelet coefficient D4, extracting an extreme value pair, obtaining the extreme value pair for accurately positioning the R wave by adopting threshold processing and filtering, restoring the position of the extreme value pair to the reconstructed ECG signal (with time shift), and finding the maximum value in the maximum value interval to obtain the R wave positioning. After the R wave is positioned, Q, S waves can be positioned by finding a minimum value point near the R wave according to the position of the R wave.
In the detection of the P wave and the T wave, the length of a detection interval is controlled by taking the positions of two adjacent R waves as a marker post and the mean value of RR intervals, so that two heart beats between the two adjacent R waves are accurately detected. A respective T-wave and P-wave. And measuring respective detection areas of the T wave and the P wave until P, Q, R, S, T waves of the ECG signal are accurately positioned, and providing a data source for later model construction.
4) Signal analysis
The incidence rate of arrhythmia in patients with heart diseases is as high as 80-100%. Therefore, the accurate judgment of arrhythmia symptoms, the rapid tracing of origin parts of arrhythmia causes and the giving of timely medical advice and treatment schemes are as follows:
dividing the data obtained in the step 3) into two first-level subclasses which are indoor blocks in excitation origin abnormality and excitation conduction abnormality respectively and are used for training a support vector machine (SVM 1);
classifying the excitation origin abnormality into sinus rhythm and ectopic rhythm respectively for training a support vector machine (SVM 2);
dividing the ectopic heart rate into an active ectopic heart rate and a passive ectopic heart rate for training the SVM 3; dividing the active ectopic heart rhythm into two categories, namely supraventricular premature beat and ventricular premature beat, and using the two categories to train the SVM 4;
Passive ectopic heart rhythms are divided into two categories, namely supraventricular escape and ventricular escape, and are used for training SVM 5;
in the invention, 7 heart rhythms, namely sinus rhythm, supraventricular premature beat, ventricular premature beat, supraventricular escape, ventricular block and ventricular flutter wave, are selected as classification targets. The processed data is classified by a Support Vector Machine (SVM) method, and the module requires that the heart rhythm is classified into 7 classes, so a binary tree classification method is adopted. All classes are first divided into two sub-classes, and the sub-classes are further divided into two sub-classes, and the process is circulated until a single class is obtained. The data is first divided into two first-level subclasses, which are the ventricular block in the excitation origin anomaly and the excitation conduction anomaly, respectively, for training the support vector machine SVM 1. The activation origin abnormalities are then classified as sinus rhythm and ectopic rhythm, respectively, for training the support vector machine SVM 2. The ectopic heart rate was classified into an active ectopic heart rate and a passive ectopic heart rate, and SVM3 was trained. Active ectopic rhythms are classified into two categories, supraventricular and ventricular premature beats, respectively, for training SVM 4. Passive ectopic rhythms are classified into two categories, supraventricular and ventricular escapes, respectively, for training SVM 5. The electrocardiosignals are divided into seven classes by five support vector machines, so that the heart condition of the sick and wounded is monitored in real time.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. Electrocardiosignal collection system based on artificial intelligence, including physiological signal collection module, wireless transmission module and intelligent terminal module, signal collection module includes electrocardiosignal collection module, its characterized in that: the physiological signal acquisition module acquires various physiological signals of the critically ill patients; the wireless transmission module performs wireless transmission after A/D conversion on the analog signals acquired by the acquisition module; the intelligent terminal processes and analyzes the received digital signals, evaluates the physiological condition of the serious sick and wounded, realizes wireless continuous monitoring of the serious sick and wounded, and automatically alarms abnormal indexes.
2. The artificial intelligence based cardiac electrical signal acquisition system of claim 1 wherein: the signal acquisition module comprises wearable equipment and a sensor arranged on the wearable equipment, the wearable equipment comprises a plurality of elastic mounting belts which are arranged in a mutual lead mode according to the structure of a human body, and the elastic mounting belts are movably spliced; the wireless transmission module adopts an anti-interference technology based on frequency agility, and avoids signal interference.
3. The artificial intelligence based cardiac electrical signal acquisition system of claim 2 wherein: the signal acquisition module also comprises a pulse, blood oxygen and electroencephalogram data acquisition module arranged on the head and a myoelectricity, cardiopulmonary sound, electrocardio and body temperature data monitoring module arranged at the corresponding position on the body.
4. The artificial intelligence based cardiac electrical signal acquisition system of claim 3 wherein: the intelligent terminal module comprises an intelligent control terminal and a central control system, the intelligent control terminal is matched with the physiological signal acquisition module through a wireless transmission module in a network mode, the physiological signal acquisition module transmits acquired data information to the central control system through wireless transmission to be stored and analyzed, the central control system feeds back the processed information to the intelligent control terminal, the intelligent control terminal is provided with an alarm module, the central control system sets threshold values for parameters such as electrocardio, pulse, blood oxygen and body temperature, and the central control system compares the processed parameters with the threshold values and feeds back the processed parameters to the alarm module of the intelligent control terminal to achieve abnormal parameter alarm.
5. The signal processing method of an artificial intelligence based cardiac signal acquisition system as claimed in claim 1, comprising:
1) an ECG signal acquisition module acquires electrocardiosignals;
2) preprocessing of ECG signals;
analyzing and denoising the ECG signal by adopting a wavelet transform method;
processing the low-scale wavelet coefficients d1 and d2 by a soft threshold value method, and eliminating high-frequency noise by a large-amplitude attenuation coefficient method;
filtering the scale 3 wavelet coefficient containing the important input signal by adopting a soft and hard threshold compromise algorithm;
3) judging the quality of the ECG signal, and screening signals with good quality;
screening a signal with good quality according to characteristics including but not limited to a threshold value and a peak part sample number; 4) electrocardiosignal feature extraction
The detection of the singular points of the ECG signal is finished based on a biorthogonal quadratic B-spline wavelet transform method; improving the positioning precision of the R wave by adopting a dynamic valve value method, analyzing a wavelet coefficient D4, extracting an extreme value pair, obtaining the extreme value pair for accurately positioning the R wave by adopting threshold processing and filtering, restoring the position of the extreme value pair to a reconstructed ECG signal, finding the maximum value in the maximum value interval, and obtaining the R wave positioning; after the R wave is positioned, Q, S waves can be positioned by searching a minimum value point near the R wave according to the position of the R wave;
The length of a detection interval is controlled by taking the positions of two adjacent R waves as a marker post and the mean value of RR intervals, so that two heart beats between the two adjacent R waves, respective T waves and P waves are accurately detected; measuring respective detection areas of the T wave and the P wave to finish accurate positioning of P, Q, R, S, T waves of the ECG signal;
5) signal analysis
Dividing the data obtained in the step 3) into two first-level subclasses which are indoor blocks in excitation origin abnormality and excitation conduction abnormality respectively and are used for training a support vector machine (SVM 1);
classifying the excitation origin abnormality into sinus rhythm and ectopic rhythm respectively for training a support vector machine (SVM 2);
dividing the ectopic heart rate into an active ectopic heart rate and a passive ectopic heart rate for training the SVM 3; dividing the active ectopic heart rhythm into two categories, namely supraventricular premature beat and ventricular premature beat, and using the two categories to train the SVM 4;
passive ectopic rhythms are classified into two categories, supraventricular and ventricular escapes, respectively, for training SVM 5.
6. The signal processing method of the artificial intelligence based electrocardiographic signal acquisition system according to claim 5, wherein: the electrocardiosignals are divided into seven classes by five support vector machines, so that the heart condition of the sick and wounded is monitored in real time.
7. The signal processing method of an artificial intelligence based cardiac signal acquisition system as recited in any of claims 1-6, further comprising: the electrocardiosignal acquisition system based on artificial intelligence is applied to wearable whole-course vital sign wireless monitoring equipment.
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