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

## TL;DR

This paper introduces a sequence model that predicts the next sleep stage to enhance sleep classification accuracy using minimal sensors, especially effective without EEG data.

## Contribution

It develops and compares n-gram and LSTM-based sleep models with beam-search decoding to improve sleep-stage classification from simple sensor data.

## Key findings

- Sleep models significantly improve classification accuracy.
- LSTM-based models outperform n-gram models.
- Effective sleep-stage prediction with EOG sensors alone.

## 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.