Abstract
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 p < 0.05 except vs. AttnSleep, p = 0.051). The KAN-SleepNet achieved an accuracy of 85.1%, F1-score of 80.0%, and Kappa of 0.792 on SleepEDF-78, and an accuracy of 82.8%, F1-score of 80.5%, and Kappa of 0.778 on ISRUC-S1. The model also exhibited strong performance in the challenging N1 stage, with F1-scores of 53.2% and 57.4% on SleepEDF-78 and ISRUC-S1, respectively. CONCLUSION: The KAN-SleepNet demonstrates superior performance in automated sleep staging, highlighting its potential as an efficient and supportive tool for clinical sleep analysis.