A 13.34 μW Event-Driven Patient-Specific ANN Cardiac Arrhythmia Classifier for Wearable ECG Sensors

• Published in: IEEE transactions on biomedical circuits and systems Vol. 14; no. 2; pp. 186 - 197
• Main Authors: Zhao, Yang, Shang, Zhongxia, Lian, Yong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Arithmetic and logic units; Arrhythmia; Arrhythmias, Cardiac - diagnosis; Artificial neural network (ANN); Artificial neural networks; biased training (BT); Biomedical monitoring; Cardiac arrhythmia; cardiac arrhythmia classification (CAC); Classifiers; CMOS; conditional grouping scheme (CGS); continuous-in-time discrete-in-amplitude (CTDA); ECG identity; EKG; electrocardiogram (ECG); Electrocardiography; Electrocardiography - instrumentation; Energy conversion efficiency; Equipment Design; Heart rate; Humans; Mathematics; Multiplication; Neural networks; Neural Networks, Computer; Neurons; Power efficiency; Signal Processing, Computer-Assisted; Training; wearable ECG sensor; Wearable Electronic Devices; Wearable sensors
• ISSN: 1932-4545; 1940-9990; 1940-9990
• DOI: 10.1109/TBCAS.2019.2954479

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 μm CMOS process, the proposed CTDA ANN-CAC can classify an arrhythmia within 252 μs at 25 MHz clock frequency with average power of 13.34 μW for 75bpm heart rate. Evaluated on MIT-BIH database, it shows over 98% classification accuracy, 97% sensitivity, and 94% positive predictivity.