@inproceedings{4f539b7f43b348a5a5c79dfb56ee28b4,
title = "Deep Identity Confusion for Automatic Sleep Staging Based on Single-Channel EEG",
abstract = "Sleep Staging (SS) is a vital step in sleep neurobiology. Though many previous approaches have been proposed to solve it, most of them suffer from poor generalization for unknown identity. In this paper, we proposed a deep identity confusion method to extract powerful task-specific and identity-invariant feature and then score sleep stages with non-linear machine learning model. With an unified CNN-LSTM structure employed for feature extraction, we implement identity confusion with an extra identity prediction branch and apply inversed gradients to frontal layers during back-propagation. Then the deep feature is used to train a XGBoost classifier. Experiments on Sleep-EDF benchmarks achieve classification accuracy and macro F1 score of 84.1\% and 78.9\%, and it suggests proposed method boost performance of origin deep learning base model and show competitive result comparing to state-of-the-art methods.",
keywords = "CNN-LSTM, EEG, Sleep Staging, deep learning, signal processing",
author = "Yu Liu and Ruiting Fan and Yucong Liu",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 14th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2018 ; Conference date: 06-12-2018 Through 08-12-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/MSN.2018.000-6",
language = "英语",
series = "Proceedings - 14th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "134--139",
booktitle = "Proceedings - 14th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2018",
address = "美国",
}