Abstract: The traditional methods of manually identifying electrocardiogram signals have problems such as high workload and recognition errors. The existing electrocardiogram monitoring equipment still faces drawbacks such as limited recognition types of electrocardiogram signals, low diagnostic accuracy, and excessive reliance on network services. In order to improve the performance of electrocardiogram monitoring systems, ECG (Electrocardiogram Signals) analysis and detection system is designed based on deep learning technology. SENet-LSTM ( Squeeze-and-Excitation Networks-Long Short Term Memory) network model is built to realize automatic diagnosis of seven categories of ECG signals. The model is deployed on an intelligent hardware platform which uses ADS1292R as the ECG acquisition module, STM32F103 as the data processing module, and Raspberry PI as the central processing module. The system uses the integrated high-performance microcomputer Raspberry PI for calculation and analysis, and provides users with offline AI(Artificial Intelligence) services. The preciseness of the model can reach 98. 44% , and the accuracy can reach 90. 00% , realizing the real-time monitoring and accurate classification of ECG, and providing accurate disease diagnosis for patients.

Key words: electrocardiogram signals, digital signal processing, deep learning, intelligent hardware platform