Spontaneous muscle activity classification with delay-based reservoir computing

Pavlidou, A., Liang, X., Ghahremani Arekhloo, N., Li, H., Marquetand, J. and Heidari, H. (2023) Spontaneous muscle activity classification with delay-based reservoir computing. APL Machine Learning, 1(4), 046112. (doi: 10.1063/5.0160927)

Abstract

Neuromuscular disorders (NMDs) affect various parts of a motor unit, such as the motor neuron, neuromuscular junction, and muscle fibers. Abnormal spontaneous activity (SA) is detected with electromyography (EMG) as an essential hallmark in diagnosing NMD, which causes fatigue, pain, and muscle weakness. Monitoring the effects of NMD calls for new smart devices to collect and classify EMG. Delay-based Reservoir Computing (DRC) is a neuromorphic algorithm with high efficiency in classifying sequential data. This work proposes a new DRC-based algorithm that provides a reference for medical education and training and a second opinion to clinicians to verify NMD diagnoses by detecting SA in muscles. With a sampling frequency of Fs = 64 kHz, we have classified SA with EMG signals of 1 s of muscle recordings. Furthermore, the DRC model of size N = 600 nodes has successfully detected SA signals against normal muscle activity with an accuracy of up to 90.7%. The potential of using neuromorphic processing approaches in point-of-care diagnostics, alongside the supervision of a clinician, provides a more comprehensive and reliable clinical profile. Our developed model benefits from the potential to be implemented in physical hardware to provide near-sensor edge computing.

• Item Type: Articles
• Additional Information: This work was supported by the UK EPSRC under the grant Industrial CASE (Grant No. EP/W522168/1), Analog Neuromorphic Processing for Biosensors.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liang, Mr Xiangpeng and Ghahremani Arekhloo, Ms Negin and Li, Haobo and Pavlidou, Miss Antonia and Heidari, Professor Hadi
• Authors: Pavlidou, A., Liang, X., Ghahremani Arekhloo, N., Li, H., Marquetand, J., and Heidari, H.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering; College of Science and Engineering > School of Physics and Astronomy
• Journal Name: APL Machine Learning
• Publisher: AIP Publishing
• ISSN: 2770-9019
• ISSN (Online): 2770-9019
• Published Online: 30 November 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in APL Machine Learning 1(4): 046112
• Publisher Policy: Reproduced under a Creative Commons License