ECG signal analysis and detection system based on deep learning

In view of the problems of large workload and easy error in traditional manual recognition of ECG signals, and the existing ECG monitoring equipment still has few ECG signal recognition types, low diagnostic accuracy, and excessive reliance on network services, in order to improve the performance of ECG monitoring equipment, an ECG (Electrocardiogram Signals) analysis and detection system is designed based on deep learning technology. The SENet-LSTM (Squeeze-and-Excitation Networks Long Short Term Memory) network model is built to realize automatic diagnosis of 7 categories of ECG signals. The model is built on an intelligent hardware platform that uses ADS1292R as ECG acquisition module, STM32F103 as data processing module, and Raspberry Pi as central processing module. The system uses the integrated high- performance microcomputer Raspberry Pi for calculation and analysis, providing users with offline artificial intelligence (AI: Artificial Intelligence) services. At the same time, the accuracy and precision of the model are and respectively 98.44%, 90.00%thereby realizing real-time detection and accurate classification of ECG, providing patients with accurate disease diagnosis.