Coronary heart disease self-diagnosis system based on electrocardiographic monitoring and back-propagation neural network

Abstract

The invention discloses a coronary heart disease self-diagnosis system based on electrocardiographic monitoring and back-propagation neural network, comprising an electrocardiographic collection terminal and a hospital monitoring center computer system, wherein the electrocardiographic collection terminal is composed of an electrocardiographic monitoring collector and a data transmission module based on wired or wireless data transmission. By means of multi-scale features of wavelet transformation, the system of the invention completes the extraction of wave peak points and the detection of ST segment in different scale decomposition coefficients by adopting a quadratic spline wavelet transformation method, thus the electrocardiographic waveform of the clinical patient can be accurately extracted. On the basis of correctively extracting characteristic points, an electrocardiogram ST segment pattern recognition model is set up by using a BP (Back-Propagation) neutral network in order to successfully recognize the pattern of the ST segment, and the initial weight and the threshold of the BP neutral network are optimized by using genetic algorithm and DNA (deoxyribonucleic acid) algorithm, thereby problem that the BP neutral network is liable to fall into local optimum in the process of training is solved, and the pattern recognition of ST segment and the diagnosis of coronary heart disease in the manner of artificial experience before are replaced.

Description

technical field [0001] The invention relates to an electrocardiographic monitoring and self-diagnosis system for coronary heart disease, in particular to a self-diagnosing system for electrocardiographic monitoring and reverse propagating neural network through the Internet for electrocardiographic data transmission. Background technique [0002] According to the analysis of the World Heart Federation, the mortality rate of coronary heart disease is much higher than that of other diseases, and it has become the main disease that threatens the safety of human life. Early diagnosis of coronary heart disease is extremely important for guiding treatment and evaluating prognosis. At present, the main diagnostic methods for coronary heart disease are coronary angiography and ECG (ie, electrocardiogram). Because coronary angiography is expensive and invasive, ECG based on noninvasive detection and analysis has become the most commonly used method. Among them, the important index ...

AI Technical Summary

Problems solved by technology

[0003] The mainstream ECG monitoring equipment currently on the market includes: ECG Holter, ECG BP machine and ECG real-time monitoring system. Although they all occupy a part of the market, they all have deficiencies: 1. ECG Holter is usually used for patients with premature beats. It can only record ECG signals, but has no real-time analysis function, nor can it perform remote monitoring. After daily use, patients must go to the hospital to read, playback, and analyze the data in Holter through special equipment; 2. Compared with Holter, ECG BP machine has real-time detection function, but the whole alarming process needs to press the "record" key of the instrument first, and dial the emergency call beside the telephone, then point the phone at the instrument, press the "send" key and a series of operations to realize the monitoring. The whole process has many steps and is complicated, which is not suitable for patients with heart disease; 3. Although the ECG real-time monitoring system solves the problem of real-time detection of ECG signals, it is limited to a small area of ​​local area network in the hospital and does not have mobility. Occasional abnormal ECG waveforms are difficult to find on the hospital bed, and long-term hospital observation will seriously affect the patient's work and normal family life, so its application range and promotion are greatly limited. In reality, quite a few patients are If the disease occurs during daily work and life, the symptoms disappear when you go to the hospital for examination, so that the abnormal ECG cannot be detected in the hospital, and the diagnosis of the condition cannot be made, which delays the best time for treatment; ECG analysis is judged by doctors relying on experience, the workload is heavy, time-consuming and cumbersome, and the accuracy depends on the doctor's personal professional level and work responsibility

Images