Automatic detection and localisation of myocardial infarction using multi-channel dense attention neural network

Myocardial infarction (MI) is a life-threatening cardiac disease. Electrocardiogram (ECG) is crucial in the clinical diagnosis of MI, facilitating early localisation and diagnosis, guiding timely intervention, and improving patient outcomes. However, most previous studies required additional denoising operations and did not propose an effective method to reduce differences between patients. Therefore, this paper presents a multi-channel dense attention neural network (MCDANN) for MI detection and localisation using 12-lead ECG signals without denoising preprocessing. MCDANN is composed of 12 parallel channels dedicated to the automatic extraction of heartbeat features. The extracted features from all channels are then aggregated for MI detection and localisation. Each channel consists primarily of the dense layers and the squeeze-and-excitation (SE) module. The dense layers enhance the reuse of feature information within the leads, whereas the SE module weakens less critical information in the ECG classification task. We conduct inter-patient and intra-patient experiments to evaluate the MCDANN model based on the PTB diagnostic database. For MI detection, MCDANN achieved 99.94% accuracy and 99.92% F1 score in intra-patient experiments and 98.27% accuracy and 96.84% F1 score in inter-patient experiments. For MI localisation, MCDANN achieved 99.85% accuracy and 99.84% F1 score in intra-patient experiments, and 81.70% accuracy and 80.73% F1 score in inter-patient experiments. Based on the experimental findings, it is clear that the MCDANN model exhibits remarkable performance in MI detection and localisation, both for intra-patient and inter-patient cases. Notably, it substantially enhances the performance of inter-patient MI localisation compared to other existing methods.

Automated summary

This paper presents a multi-channel dense attention neural network (MCDANN) for myocardial infarction (MI) detection and localization using 12-lead ECG signals without denoising preprocessing. The MCDANN model exhibits remarkable performance in MI detection and localization, both for intra-patient and inter-patient cases. Notably, it substantially enhances the performance of inter-patient MI localization compared to other existing methods.