Design And Implementation Of A Disturbance-free Arrhythmia Monitoring System

In recent years,with the continuous increase in the incidence and mortality of cardiovascular diseases worldwide,people have started to pay attention to their physical conditions.It is conveniently for heart disease patients to use the personal heart health monitoring equipment for heart disease prevention screening and auxiliary diagnosis in daily life.However,the popular portable ECG detection devices in market(such as smart bracelets,etc.)do not have the functions,such as ECG signal analysis and disease diagnosis warning.Therefore,the development of heart health monitoring system products with perfect functions and suitable for non-professional occasions has become an important research topic for researchers.This article focuses on the prevention and auxiliary diagnosis of arrhythmia,and further studies the non-contact ECG signal measurement method based the capacitive coupling principle and the arrhythmia classification algorithm based on the convolutional neural network structure.We design a non-disruptive ECG signal detection system with arrhythmia monitoring function.The main contents are as follows:1.By studying the impedance models of different types of ECG signal measurement electrodes,a coupled measurement electrode model based on parallel plate capacitors was proposed based on theoretical analysis and experimental verification.Based on the measurement technology of human bioelectric signals and the principle of capacitive coupling,the human ECG signal detection model based on capacitive measuring electrode is applied.2.According to the application requirements of disturbance-free monitoring,we designs a complete set of non-contact ECG signal measurement equipment.This device consists of a coupled measuring electrode sensor and a signal preprocessing circuit.Therefore,we conducted a functional verification experiment with extremely high input impedance designed in this paper under different test conditions.Experimental results show that the sensor can extract a complete human ECG signal on the outside of light clothing.According to the characteristic parameters of the ECG signal,a series of circuits for signal amplification,denoising and transmission are designed,specifically: signal conditioning circuit,signal transmission circuit and power supply circuit.3.Based on the research of arrhythmia classification method based on convolutional neural network,this paper designs a two-dimensional convolutional neural network model that follows VGGNet structure.Different from the common model parameter adjustment methods,we change the input sample type(ECG grayscale image and ECG time-frequency image)to obtain a training model with better performance.In the classification verification experiment on 8 kinds of target heart beats,the model obtained an average accuracy of 99.78%,and also achieved good results on other evaluation.4.We design an interactive PC application with good signal processing capabilities.The ECG acquisition device is connected to the PC processing terminal by wireless communication.At the same time,the remote server was used to build a cloud service platform with functions such as data analysis,information storage,management,and disease-assisted diagnosis.Eventually,a heart health monitoring system integrating real-time ECG signal collection and arrhythmia analysis is integrated.The results show that the system can accurately measure the human body’s ECG signals and provide auxiliary diagnostic advice for arrhythmia without affecting the normal work.