As sleep disorders are becoming more prevalent there is an urgent need to classify sleep stages in a less disturbing way.In particular, sleep-stage classification using simple sensors, such as single-channel electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), or electrocardiography (ECG) has gained substantial interest. In this study, we proposed a sleep model that predicts the next sleep stage and used it to improve sleep classification accuracy. The sleep models were built using sleep-sequence data and employed either statistical n-gram or deep neural network-based models. We developed beam-search decoding to combine the information from the sensor and the sleep models. Furthermore, we evaluated the performance of the n-gram and long short-term memory (LSTM) recurrent neural network (RNN)-based sleep models and demonstrated the improvement of sleep-stage classification using an EOG sensor. The developed sleep models significantly improved the accuracy of sleep-stage classification, particularly in the absence of an EEG sensor.
@article{arxiv.2302.12709,
title = {Sleep Model -- A Sequence Model for Predicting the Next Sleep Stage},
author = {Iksoo Choi and Wonyong Sung},
journal= {arXiv preprint arXiv:2302.12709},
year = {2023}
}