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

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 a

SUBMITTER: Xiong Z 

PROVIDER: S-EPMC12627363 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature