KAN-SleepNet: A deep learning model combining Kolmogorov-Arnold Networks and bidirectional LSTM for automated sleep
Zhenliang Xiong1,2,3, Yuxuan Gou4, Yinglin Zhou2,3
1Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province, Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China.
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.
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