EKGNet: A 10.96μW Fully Analog Neural Network for Intra-Patient
  Arrhythmia Classification
Benyamin Haghi Lin Ma Sahin Lale Anima Anandkumar Azita Emami
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.