ALSGate: An Efficient Gated Mixture-of-Experts Model for Reliable ALS Detection Using EMG Signals.

Authors

Sandipa Chowdhury, Sudipto Pramanik, Sudha Bhattacharjee, Motasim Billah

Published in

Healthcare technology letters. Volume 13. Issue 1. Pages e70097. Epub Sep 03, 2026.

Abstract

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder affecting motor neurons, resulting in neuromuscular weakness and paralysis. Electromyography (EMG) is of vital importance for the detection of ALS. In this paper, a refined mixture of experts is proposed that automatically discriminates ALS patients from non-ALS cases using clinical EMG signals from the N2001 EMGLAB open-access dataset. The architecture consists of a 1D convolutional neural network, a temporal convolutional network and a spectrogram-based CNN to collectively learn localised temporal, long-range temporal and spectral features from EMG activity. A gating mechanism dynamically weights expert contributions and performs significantly better than equal-weight fusion. Training with focal loss and exponential moving average stabilisation addresses class imbalance and improves convergence. The proposed approach reached an AUROC of 0.9992, an F1-score of 0.9903 and a balanced accuracy of 0.9905, demonstrating strong discriminative performance and potential for real-time clinical applications.

PMID:
42694829
Bibliographic data and abstract were imported from PubMed on 04 Sep 2026.

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