A neuromorphic model with delay-based reservoir for continuous ventricular heartbeat detection

Liang, X., Li, H., Vuckovic, A., Mercer, J. and Heidari, H. (2022) A neuromorphic model with delay-based reservoir for continuous ventricular heartbeat detection. IEEE Transactions on Biomedical Engineering, 69(6), pp. 1837-1849. (doi: 10.1109/TBME.2021.3129306)

Abstract

There is a growing interest in neuromorphic hardware since it oers a more intuitive way to achieve bio-inspired algorithms. This paper presents a neuromorphic model for intelligently processing continuous electrocardiogram (ECG) signals. This model aims to develop a hardware-based signal processing model and avoid employing digitally intensive operations, such as signal segmentation and feature extraction, which are not desired in an analogue neuromorphic system. We apply delay-based reservoir computing as the information processing core, along with a novel training and labelling method. Dierent from the conventional ECG classication techniques, this computation model is an end-to-end dynamic system that mimics the real-time signal ow in neuromorphic hardware. The input is the raw ECG stream, while the amplitude of the output represents the risk factor of a ventricular ectopic heartbeat. The intrinsic memristive property of the reservoir empowers the system to retain the historical ECG information for high-dimensional mapping. This model was evaluated with the MIT-BIH database under the inter-patient paradigm and yields 81% sensitivity and 98% accuracy. Under this architecture, the minimum size of memory required in the inference process can be as low as 3.1 MegaByte(MB) because the majority of the computation takes place in the analogue domain. Such computational modelling boosts memory eciency by simplifying the computing procedure and minimizing the required memory for future wearable devices.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Mercer, Dr John and Li, Haobo and Heidari, Professor Hadi and Liang, Xiangpeng and Vuckovic, Dr Aleksandra
• Authors: Liang, X., Li, H., Vuckovic, A., Mercer, J., and Heidari, H.
• College/School: College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health; College of Science and Engineering > School of Engineering > Biomedical Engineering; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: IEEE Transactions on Biomedical Engineering
• Publisher: IEEE
• ISSN: 0018-9294
• ISSN (Online): 1558-2531
• Published Online: 19 November 2021
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in IEEE Transactions on Biomedical Engineering 69(6): 1837-1849
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
304896		EPSRC-IAA: Early Stage Commercialisation of a PET Imaging Agent for the Detection of Cardiovascular Disease and Cancer	Andrew Sutherland	Engineering and Physical Sciences Research Council (EPSRC)	EP/R511705/1	Chemistry