Dynamic electrocardiogram detection and diagnosis based on improved dilated convolutional network

Electrocardiograms (ECGs) are crucial for promptly detecting heart disease, assessing cardiac function, and guiding treatment, making them a key clinical tool. However, traditional ECG monitoring systems are confronted with numerous challenges. This paper presents a dynamic ECG monitoring and diagnostic system that integrates the two major developmental directions of ECG monitoring systems, namely portability and intelligent diagnosis. The portable acquisition terminal is compact, measuring only 80 mm × 100 mm and weighing approximately 150 g, so patients can easily carry it. The terminal connects to the host computer via Wi-Fi, and the acquired data is wirelessly transmitted for subsequent processing. For arrhythmia classification, this paper proposes a network architecture model that combines hybrid dilated convolution with hybrid attention mechanisms. Our hybrid dilated convolution module reduces the number of network parameters, solving key problems with traditional dilated convolution. The hybrid attention module precisely captures global dependencies in spatial and channel dimensions, enhancing diagnostic accuracy. In practical testing, the system demonstrates excellent performance. The AD8232 chip, in conjunction with Hierarchical Kalman filtering, efficiently processes ECG signals and successfully suppresses noise interference. The system maintains high accuracy, with resting heart rate measurements staying within ±2% of the true value, and exercise heart rate within ±4%. The display interface of the host computer presents clear ECG waveforms and accurate classification results. The model achieves a per-beat overall classification accuracy of 99.28% under the AAMI EC57 five-class standard, with a macro-averaged F1-score of 96.45% and a macro-averaged MCC of 0.9561 across the four evaluable classes (N, S, V, F), indicating high stability and statistical reliability.