EKGNet: A 10.96μW Fully Analog Neural Network for Intra-Patient Arrhythmia Classification.

We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrythmia classification architecture that achieves high accuracy with low power consumption. The proposed architecture leverages the energy efficiency of transistors operating in the subthreshold region, eliminating the need for analog-to-digital converters (ADC) and static random-access memory (SRAM). The system design includes a novel analog sequential Multiply-Accumulate (MAC) circuit that mitigates process, supply voltage, and temperature variations. Experimental evaluations on PhysionNet’s MIT-BIH and PTB Diagnostics datasets demonstrate the effectiveness of the proposed method, achieving an average balanced accuracies of 95% and 94.25% for intra-patient arrhythmia classification and myocardial infarction (MI) classification, respectively. This innovative approach presents a promising avenue for developing low power arrythmia classification systems with enhanced accuracy and transferability in biomedical applications.