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A 13.34μW Event-driven Patient-specific ANN Cardiac Arrhythmia Classifier for Wearable ECG Sensors.

07:00 EST 28th November 2019 | BioPortfolio

Summary of "A 13.34μW Event-driven Patient-specific ANN Cardiac Arrhythmia Classifier for Wearable ECG Sensors."

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 computation. 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 of 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 power efficiency. Verified in FPGA and synthesized in 0.18μm CMOS process, the proposed CTDA ANN-CAC can classify an arrhythmia within 252's at 25MHz clock frequency with average power of 13.34μW 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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This article was published in the following journal.

Name: IEEE transactions on biomedical circuits and systems
ISSN: 1940-9990
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