ECG Classification Exercise Health Analysis Algorithm Based on GRU and Convolutional Neural Network

Sudden cardiac death (SCD) is one of the main causes of death in athletes during exercise and physical activity. By analyzing the results of the ECG classification, doctors can promptly detect the presence of cardiovascular diseases and implement appropriate treatment measures, thereby reducing disease progression and the occurrence of complications. Previous studies have typically used machine learning methods that often required manual feature selection, which could be subjective, time-consuming, and limited in scope. Additionally, the relationships between features may be overlooked, leading to a decrease in model performance. Therefore, our proposed approach automatically learns and selects relevant features, avoiding the issues associated with manual feature selection. In this paper, we introduce the Grunet deep learning network model, which utilizes convolutional neural networks to automatically extract features from ECG signals, thereby enhancing feature utilization and representation capabilities. Given the time-sensitive nature of ECG signals, we introduce gated recurrent units (GRU) to better capture these features. The gating mechanism of GRU helps manage information flow and facilitates capturing long-term dependencies. In our experiments conducted on the MIT-BIH Arrhythmia Database and the European ST-T Database, our proposed method achieved an overall classification accuracy of 99.47% on the European ST-T Database, with a precision of 98.76% and a recall of 97.92% on the MIT-BIH Arrhythmia Database. The classification accuracy for classes N and Q reached 100%. The experimental results demonstrate that our method outperforms existing techniques, significantly reducing the cost of manual intervention and improving the accuracy of heartbeat classification.