Research On Denoising Algorithm Of ECG Signal Based On Convolutional Auto-encoder Neural Network

Cardiovascular disease has the characteristics of high morbidity,high lethality,and high recurrence rate.It has become the world's leading mortality rate.The number of deaths caused by it each year accounts for about 31% of the global total.The number of people suffering from cardiovascular diseases is still increasing,showing a trend of youth,posing a serious threat to human health.Electrocardiogram,as a graphic technology for recording human heart electrical activity,has become one of the important methods for the detection and diagnosis of cardiovascular diseases due to its non-invasive characteristics.However,it will be subject to various disturbances during the acquisition process,resulting in waveform distortion of the collected ECG signals,which will affect the diagnosis and analysis of the disease to a certain extent.Therefore,it is of great significance to solve the problem of ECG signal denoising.In order to reduce the influence of ECG noise,this paper proposes an ECG signal denoising algorithm based on convolutional auto-encoder neural network.The main research contents of the paper are as follows:(1)An ECG signal denoising algorithm based on convolutional auto-encoder neural network is proposed.Combining a convolutional neural network with an autoencoder,using the encoding and decoding characteristics of the autoencoder to build a deep neural network through a convolution method to learn the end-to-end mapping from noisy ECG signals to clean ECG signals,Learn complete convolution and deconvolution mapping end-to-end,get clean ECG signals from noisy ECG signals,and complete the noise removal of ECG signals.The results show that the denoising algorithm based on convolutional auto-encoder neural network achieves good denoising effect,and it also has certain advantages for noise filtering of variable heartbeats with rich pathological information.(2)In view of the fact that the type of ECG signal noise is variable and unknown in practical clinical applications,and it is difficult to take a single method to properly handle it,a blind noise reduction method for ECG signals based on a convolutional auto-encoder neural network is proposed.First,use the noise classification method to classify the noise type of the ECG signal,and then select the CAENN noise reducer that is closest to the noise type to perform effective denoising.It is verified by experiments that the noise reduction of noisyECG signals by the algorithm in this chapter can improve the noise reduction accuracy of ECG signals with mixed noise while maintaining the noise reduction advantage of the variable heartbeat that plays a key role in the diagnosis of the disease.(3)The noise reduction algorithm proposed in this paper is applied to the ECG monitoring platform independently developed by the research group.The method of this paper is tested with actual collected ECG data to verify the practicability and reliability of the noise reduction algorithm.