.png){kind=icon}
返回
Interpretable Sleep Stage Classification Based on Layer-Wise Relevance Propagation
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2024.3370799.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Layer-wise relevance propagation (LRP)
model interpretability
neural networks
sleep stage classification
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
机构
引用论文
Black Intellectuals' Critique of Early Mental Testing: A Little-Known Saga of the 1920s黑人知识分子对早期心理测试的批判: 一个鲜为人知的20世纪20年代传奇
PREAI
分享收藏Rules for Scoring Respiratory Events in Sleep: Update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events睡眠呼吸事件评分规则: 更新2007 AASM睡眠及相关事件评分手册
OAAI
OAAI分享收藏Performance Assessment of Five Different Soil Moisture Sensors under Irrigated Field Conditions in Oklahoma.png)
Sensors
OAAI
SensorsIF0
OAAI分享收藏
查看更多内容