A resource-efficient ECG diagnosis model for mobile health devices

Abstract

Mobile health devices with automatic electrocardiogram diagnosis models facilitate long-term cardiac monitoring and enhance the sensitivity of detecting paroxysmal cardiovascular disease. However, these devices often encounter limitations in terms of computational power and battery life. Moreover, iterations of mobile health device versions may result in changes to their computational limitations, leading to additional costs for model design and training. Therefore, it is crucial to develop lightweight models that enable efficient inference under resource constraints and are compatible with various devices. To address these challenges, we propose a resource-efficient and device-adaptive electrocardiogram diagnosis model based on a multi-classifier dynamic neural network. This model achieves efficient computation by exiting simple samples early and can adapt to the computational limits of various devices by adjusting predefined thresholds. Furthermore, we propose two novel approaches, namely rethinking structure and feature-based knowledge distillation, which enhance the collaboration among classifiers and improve the overall performance. The proposed model is evaluated using three electrocardiogram datasets: PTB-XL (AUC = 0.9706), ICBEB2018 (AUC = 0.964), and MIT-BIH arrhythmia (ACC = 0.994, F1 = 0.961). The results consistently demonstrate the superiority of the proposed model over the comparative models across all three datasets, with average improvements of 2.4%, 0.5%, and 1.3% respectively.