A 1D Squeezenet-Based Edge Computing System for Arrhythmia Classification in the Elderly

This paper proposes an edge computing system for real-time arrhythmia classification in the elderly, leveraging a novel one-dimensional SqueezeNet optimized for ECG signal. The special design of Fire Module leverages point-wise (1x1) convolutions to compress channel dimensions and combines them with wider (3x1) convolutions to effectively balance parameter efficiency and temporal feature extraction. By directly receiving the raw input, unnecessary signal conversion operations are effectively avoided. The compact architecture (0.36 million parameters) attains 91.41% accuracy, 99.56% recall, 98.56% precision, and 99.06% F1 score on the MIT-BIH Arrhythmia Database. These results show better performance for classifying previously unseen data of the proposed model than the state-of-the-art works. Evaluated only on elderly subjects, accuracy rises to 96.25% without any decline in other performance metrics, emphasizing the necessity of conducting separate training for the ECG of the elderly. The trained model is quantized and deployed to an Arm Cortex-M7 processor, yielding an inference latency of 146 ms with only 121 kB RAM usage to enable on-device processing. This demonstrates that high-performance elderly-specific arrhythmia classification can be realized on resource-constrained edge computing system without cloud dependency.