EKGNet: A 10.96μW Fully Analog Neural Network for Intra-Patient Arrhythmia Classification
This work addresses the need for energy-efficient, accurate arrhythmia detection in biomedical applications, representing a novel integration of analog computing and deep learning rather than an incremental improvement.
The paper tackled the problem of low-power arrhythmia classification from ECG signals by proposing EKGNet, a fully analog neural network, achieving average balanced accuracies of 95% for arrhythmia and 94.25% for myocardial infarction classification with a power consumption of 10.96μW.
We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives 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 PhysioNet's MIT-BIH and PTB Diagnostics datasets demonstrate the effectiveness of the proposed method, achieving average balanced accuracy 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 arrhythmia classification systems with enhanced accuracy and transferability in biomedical applications.