Sleep Model -- A Sequence Model for Predicting the Next Sleep Stage

• URL: http://arxiv.org/abs/2302.12709v1
• Date: Fri, 17 Feb 2023 07:37:54 GMT
• Title: Sleep Model -- A Sequence Model for Predicting the Next Sleep Stage
• Authors: Iksoo Choi and Wonyong Sung
• Abstract summary: 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.
• Score: 18.059360820527687
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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