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

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.