Spontaneous muscle activity classification with delay-based reservoir computing

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.