文献类型：期刊文献

英文题名：A Developed LSTM-Ladder-Network-Based Model for Sleep Stage Classification

作者：Li, Ruichen[1];Wang, Bei[1];Zhang, Tao[2];Sugi, Takenao[3]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Dept Automat, Shanghai 200237, Peoples R China;[2]Tsinghua Univ, Dept Automat, Beijing 100086, Peoples R China;[3]Saga Univ, Fac Sci & Engn, Dept Elect & Elect Engn, Saga 8408502, Japan

年份：2023

卷号：31

起止页码：1418

外文期刊名：IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING

收录：;EI(收录号：20231013665544);WOS:【SCI-EXPANDED(收录号:WOS:000940121400008)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61773164 and in part by the National Key Research and Development Program of China under Grant 2017YFB13003002.

语种：英文

外文关键词：Sleep; Electroencephalography; Feature extraction; Brain modeling; Recording; Data models; Electrooculography; Sleep stage; EEG; long short-term memory network; ladder network; transductive learning

摘要：Sleep staging is crucial for diagnosing sleep-related disorders. The heavy and time-consuming task of manual staging can be released by automatic techniques. However, the automatic staging model would have a relatively poor performance when working on unseen new data due to individual differences. In this research, a developed LSTM-Ladder-Network (LLN) model is proposed for automatic sleep stage classification. Several features are extracted for each epoch and combined with the following epochs to form a cross-epoch vector. The long short-term memory (LSTM) network is added into the basic ladder network (LN) to learn the sequential information of adjacent epochs. The developed model is implemented based on a transductive learning scheme to avoid the issue of accuracy loss caused by individual differences. In this process, the labeled data pre-trains the encoder, and the unlabeled data re- fine the model parameters by minimizing the reconstruction loss. The proposed model is evaluated on the data from public database and hospital. Comparison experiments were conducted where the developed LLN model achieved rather satisfied performance while dealing with the unseen new data. The obtained results demonstrate the effectiveness of the proposed approach in addressing individual differences. This can improve the quality of automatic sleep staging when assessed on different individuals and has strong application potential as a computer aided approach for sleep staging.