Interpretable Sleep Stage Classification Based on Layer-Wise Relevance Propagation

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.