Research on ECG Signal Classification based on Wavelet Theory

ECG as a direct reflection of cardiac electrical activity, plays a crucial role in the diagnosis of cardiovascular diseases. The traditional ECG diagnostic method relies on the professional knowledge and experience of doctors, and has subjectivity and efficiency limitations. If advanced algorithms can be used to achieve automatic, fast, and accurate recognition of ECG types, it is of great significance for managing personal health status and assisting doctors in making more efficient diagnoses. This project aims to use wavelet transform and wavelet time scattering network to achieve automatic recognition and classification of ECG signals, thereby promoting the intelligence of medical diagnosis. The project preprocesses signals obtained from a large ECG dataset, extracts feature information, and constructs a model suitable for processing time series data. By training and optimizing the model, it can accurately recognize different types of ECG waveforms and patterns. After the model training is completed, an independent test set is used for performance evaluation to ensure the reliability and generalization ability of the model, which is displayed through GUI. This project is divided into three categories for the first time, with an accuracy rate of 99.92%; Further subdividing into four categories, the accuracy rate reached 96.69%. The accuracy of electrocardiogram classification and recognition in this study is high, which can not only improve the accuracy and efficiency of cardiovascular disease diagnosis, reduce the workload of doctors, but also provide strong support for early detection and treatment of heart disease, and is expected to bring revolutionary changes to the medical field.