Design of ECG Signal Diagnosis Algorithm Based on Neural Networks

Traditional electrocardiogram (ECG) analysis methods rely heavily on the experience and expertise of physicians, leading to limitations such as high subjectivity and prolonged processing times. To address these issues, we propose the design of a diagnosis algorithm for ECG signals utilizing neural networks. This algorithm involves preprocessing, denoising, and feature extraction of the ECG signals, followed by training and analysis using a deep learning neural network model. The objective is to accurately identify and diagnose five common heart rate conditions. Additionally, a visualization interface is developed using PyQt to facilitate the imaging, processing, and display of the diagnosis results for ECG signals.