Lightweight self-attention and deep gated neural network (LSA-DGNet) for multiple neurological disease detection

Abstract

Detecting neurological diseases is an important task in modern medicine, for which it is crucial to accurately model the temporal distributions of disease genesis. In prior methodologies, temporal patterns are used in feature effects and limiting assumptions such as proportionate risks. We introduce a new methodology for neural disease diagnosis, known as LSA-DGNet (Lightweight Self-Attention based on Deep Gated Network). LSA-DGNet utilizes a deep gated neural network module to model nonlinear and time-lagged effects of variables on disease outcomes. We combined multi-scale time-aware self-attention modules with scaled dot-product self-attention modules so that the parallel structures could provide an integrated self-attention mechanism to improve data perception. LSA-DGNet addresses both issues and, hence, sets a new benchmark for real-time, accurate detection of neurological diseases. Unlike existing approaches, LSA-DGNet integrates a lightweight multi-scale time-aware self-attention mechanism with deep gated neural networks, enabling improved modeling of temporal dependencies in noisy EEG data. This design allows for accurate and efficient detection of multiple neurological diseases, validated on five real-world datasets, setting new benchmarks in classification performance. With up to 250 frames a second, it indicant large progress in computational efficiency—game-changer potential—and clinical applications. The entire framework opens up new opportunities for early diagnosis and more tailored treatment strategies and simply revolutionizes how neurological diseases are detected and treated.