KAN-SleepNet: A deep learning model combining Kolmogorov-Arnold Networks and bidirectional LSTM for automated sleep staging using EEG signals.

Purpose: Sleep staging is critical for assessing sleep quality and diagnosing sleep disorders. However, manual annotation is both time-consuming and labor-intensive, highlighting the need for efficient automated solutions. This study proposes Kolmogorov-Arnold Networks (KAN)-SleepNet, a hybrid deep learning model designed for automated sleep stage classification using single-channel electroencephalogram (EEG) signals. Methods: The KAN-SleepNet consists of two primary components: (1) a ConvKAN block that integrates convolutional neural networks with KAN to effectively extract discriminative features from EEG signals, and (2) a Bidirectional Long Short-Term Memory layer to capture temporal dependencies across sleep stages. The model was trained and evaluated using two publicly available datasets: the SleepEDF-78 dataset, comprising 153 recordings from 78 subjects, and the ISRUC-S1 dataset, consisting of 100 recordings from 100 subjects. SleepEDF-78 was annotated according to the Rechtschaffen and Kales criteria, whereas ISRUC-S1 followed the American Academy of Sleep Medicine guidelines. Performance was assessed using accuracy, F1-score, and Cohen's Kappa (κ), and results were compared against baseline models, including SleepEEGNet, DeepSleepNet, TinySleepNet, AttnSleep, GraphSleepNet, and MSTGCN. Results: Experimental results demonstrate that KAN-SleepNet outperforms existing baseline models across both datasets (all Conclusion: The KAN-SleepNet demonstrates superior performance in automated sleep staging, highlighting its potential as an efficient and supportive tool for clinical sleep analysis.