Research on ECG signal processing under complex noise based on machine learning algorithm

Accurate acquisition of electrocardiogram (ECG) signals is crucial for the diagnosis of cardiovascular diseases. However, in complex environments and nonexercise states, ECG signals are often interfered by a variety of noises, which poses challenges to the stability and accuracy of the signals. This study aims to explore the application of machine learning algorithms in improving the quality of ECG signal acquisition, especially the ability to acquire valid ECG signals in complex noise environments.This paper classifies and analyses the types of noise that may be encountered during ECG signal acquisition, including myoelectric interference, baseline drift, power line interference, etc., and explores the impact of these noises on the quality of ECG signals, investigates the application of machine learning algorithms in ECG signal denoising and feature extraction, and proposes a framework for ECG signal processing based on the Deep Residual Shrinkage Network combined with the Fully Convolutional Self-Coder and Decoder for ECG signals in a complex noise environment. signal processing framework in a complex noise environment. Using DResNet to remove strong noise and retain ECG signal details, and using FCN-DAE to reconstruct ECG signals, this denoising algorithm demonstrates good denoising ability and reconstruction and restoration of ECG signals on the MIB-HIM dataset. Especially on wearable ECG acquisition devices, recognisable ECG data can be obtained in complex noise environments.