Journal
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS
Volume 14, Issue 2, Pages 186-197Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBCAS.2019.2954479
Keywords
Electrocardiography; Wearable sensors; Training; Neurons; Biomedical monitoring; Databases; Artificial neural network (ANN); biased training (BT); cardiac arrhythmia classification (CAC); conditional grouping scheme (CGS); continuous-in-time discrete-in-amplitude (CTDA); electrocardiogram (ECG); ECG identity; wearable ECG sensor
Funding
- NSERC [500504107]
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Artificial neural network (ANN) and its variants are favored algorithm in designing cardiac arrhythmia classifier (CAC) for its high accuracy. However, the implementation of ultralow power ANN-CAC is challenging due to the intensive computations. Moreover, the imbalanced MIT-BIH database limits the ANN-CAC performance. Several novel techniques are proposed to address the challenges in the low power implementation. Firstly, continuous-in-time discrete-in-amplitude (CTDA) signal flow is adopted to reduce the multiplication operations. Secondly, conditional grouping scheme (CGS) in combination with biased training (BT) is proposed to handle the imbalanced training samples for better training convergency and evaluation accuracy. Thirdly, arithmetic unit sharing with customized high-performance multiplier improves the power efficiency. Verified in FPGA and synthesized in 0.18 mu m CMOS process, the proposed CTDA ANN-CAC can classify an arrhythmia within 252 mu s at 25 MHz clock frequency with average power of 13.34 mu W for 75bpm heart rate. Evaluated on MIT-BIH database, it shows over 98% classification accuracy, 97% sensitivity, and 94% positive predictivity.
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