A1 Journal article (refereed)
Interpretable Sleep Stage Classification Based on Layer-Wise Relevance Propagation (2024)


Zhou, D., Xu, Q., Zhang, J., Wu, L., Xu, H., Kettunen, L., Chang, Z., Zhang, Q., & Cong, F. (2024). Interpretable Sleep Stage Classification Based on Layer-Wise Relevance Propagation. IEEE Transactions on Instrumentation and Measurement, 73, Article 3511710. https://doi.org/10.1109/tim.2024.3370799


JYU authors or editors


Publication details

All authors or editorsZhou, Dongdong; Xu, Qi; Zhang, Jiacheng; Wu, Lei; Xu, Hongming; Kettunen, Lauri; Chang, Zheng; Zhang, Qiang; Cong, Fengyu

Journal or seriesIEEE Transactions on Instrumentation and Measurement

ISSN0018-9456

eISSN1557-9662

Publication year2024

Publication date27/02/2024

Volume73

Article number3511710

PublisherInstitute of Electrical and Electronics Engineers (IEEE)

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1109/tim.2024.3370799

Publication open accessNot open

Publication channel open access

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/100095


Abstract

Numerous deep learning-based methodologies have been proposed to facilitate automatic sleep stage classification tasks. Nevertheless, the black-box nature of these approaches is one of the skeptical factors hindering clinical application. Toward model interpretability, this study presents a novel interpretable sleep stage classification scheme based on layer-wise relevance propagation (LRP). We first adopt the short-time Fourier transform (STFT) to convert the raw electroencephalogram (EEG) signals to the time-frequency images, which could visually demonstrate EEG patterns of each sleep stage. Moreover, we introduce an efficient convolutional neural network (CNN)-based model, namely MSSENet, that assembles with the multiscale CNN (MSCNN) module and residual squeeze-and-excitation (R-SE) block for the image input. The LRP method is eventually applied to evaluate the contribution of each frequency pixel in the input time-frequency image to the model prediction. Experimental findings show that the MSSENet could outperform or achieve comparable performance to other state-of-the-art approaches on three polysomnography (PSG) datasets. Furthermore, through utilizing the heat mapping, the LRP-based explainability results validate the high relevance of specific EEG patterns to the prediction of the corresponding sleep stage, which is consistent with the sleep scoring guidelines.


KeywordssleepclassificationEEGsignal analysissignal processing

Free keywordssleep; brain modeling; electroencephalography; time-frequency analysis; data models; recording; feature extraction


Contributing organizations


Ministry reportingYes

VIRTA submission year2024

Preliminary JUFO rating3


Last updated on 2025-08-02 at 20:06