SinTransNet: an EEG-based deep learning framework for infantile epileptic spasms syndrome detection

Infantile Epileptic Spasms Syndrome (IESS) represents a severe form of developmental epileptic encephalopathy in infancy, characterized by clusters of spasms and hypsarrhythmia patterns on electroencephalogram (EEG), which often lead to long-term neurodevelopmental impairments if not diagnosed promptly. The inherent non-stationarity and polymorphic complexity of EEG signals complicate interpretation, resulting in time-consuming and error-prone diagnostics that hinder timely therapeutic interventions. To address these challenges, we propose SinTransNet, an innovative EEG-based deep learning framework that combines multi-band signal decomposition, adaptive sinusoidal convolutions, and Transformer-based attention mechanism. This architecture decomposes EEG into five key frequency bands (δ, θ, α, β, γ) to isolate oscillatory features such as spike-and-wave complexes, employs sinusoidal convolutions for frequency-adaptive feature extraction, and utilizes Transformer attention to capture inter-band correlations and long-range dependencies essential for accurate IESS detection. Evaluated on a proprietary dataset comprising 129 EEG recordings with 1,941 epileptic spasm events, SinTransNet demonstrates superior performance with average accuracy of 85.69%, sensitivity of 80.55%, and specificity of 90.76%. By providing an automated, efficient tool for IESS identification, SinTransNet holds promise for enhancing clinical workflows and supporting early interventions in pediatric neurology.