Design And Implementation Of Arrhythmia Diagnosis Algorithm Based On Deep Learning

Arrhythmia is a common cardiovascular syndrome.The correct identification of arrhythmias is of great significance for the prevention of cardiovascular diseases.Electrocardiogram(ECG)is a kind of medical monitoring technology reflecting cardiac activity,which is widely used in the detection of arrhythmia.ECG is used to observe whether the ECG signals are abnormal and whether abnormal heart beats are generated,so as to achieve the effect of early prevention or diagnosis of cardiovascular diseases.In the clinical examination,due to the influence of interference signals such as power frequency and myoelectricity,the ECG signal usually contains various noises,which brings certain difficulties to the reading of the ECG.At the same time,due to the increase in the number of electrocardiograms,the subjectivity of medical staff and the complex form of arrhythmia,traditional artificial mapping methods are less efficient and there is the possibility of misdiagnosis and missed diagnosis.Therefore,in order to save patients' time,reduce the pressure of experts and improve the efficiency of ECG reading,it is very important to design a classification algorithm for automatic diagnosis of arrhythmia.Based on the characteristics of the deep learning model,this paper has done an in-depth study on the design of the automatic diagnosis algorithm for arrhythmia.The main contents include:1.The ECG filtering process is performed by using a wavelet transform algorithm.ECG signals are often mixed with various interference signals and baseline drift.The threshold processing is performed by wavelet decomposition,and high frequency noise can be suppressed.At the same time,it can improve the baseline drift in the signal.2.A key feature is extracted and a waveform coding rule is designed.The morphological change of the waveform is an important basis for diagnosing arrhythmia,and the morphological features of the waveform are extracted for morphological determination.According to the transmission sequence of cardiac electrical signals,a waveform coding method based on time series arrangement is proposed to more accurately describe the waveform changes caused by arrhythmia.3.Constructing a diagnosis model of arrhythmia based on deep learning.Combined with the characteristics of time series data,the DBiLSTM learning model was constructed with long and short memory neural network as the basic unit.The algorithm model takes the timing ECG coding as the input and the diagnosis result as the output,which realizes the effective diagnosis of the six types of heart beats.4.Compared with support vector machine,BP neural network and convolutional neural network model,the results show that the model built in this paper has a fast convergence speed,and achieves better classification results.