Temporal Focal Modulation Networks for Sleep Stage Scoring

Abstract

Accurate sleep stage scoring is crucial for diagnosing and treating sleep disorders, yet traditional manual methods are time-consuming and susceptible to variability. While recent advancements in machine learning and deep learning have enhanced automated sleep stage detection, many approaches still rely on handcrafted features and encounter limitations when processing full-night data. In this paper, we introduce a novel many-to-many classification framework that leverages a temporal focal modulation network for efficient and accurate sleep stage scoring. Our model, SleepFocalNet, processes full-night single-channel EEG signals and predicts sleep stages for all epochs simultaneously. SleepFocalNet is composed of three key components: a convolution block for local feature extraction, a focal modulation block for long-range temporal modeling, and a classification block for final predictions. We evaluated SleepFocalNet on Sleep Heart Health Study (SHHS), SleepEDF-20, and SleepEDF-78 datasets, achieving state-of-the-art performance. On SHHS, SleepFocalNet attained an accuracy of 0.888 and an F1-score of 0.815. On SleepEDF-20, it obtained an accuracy of 0.885 and an F1-score of 0.836. On SleepEDF-78, it outperformed other models with an accuracy of 0.855 and an F1-score of 0.800. This study represents the first application of temporal focal modulation networks in sleep stage scoring. Additionally, we conducted an extensive analysis of various network configurations to assess the impact of different architectural choices on performance. The results validate the potential of our approach to enhance the reliability and scalability of automated sleep stage scoring, offering a robust alternative to existing methods.