# A deep learning software tool for automated sleep staging in rats via single channel EEG

## TL;DR

A deep learning tool automates sleep stage classification in rats using single-channel EEG data, reducing manual work and enabling faster sleep research.

## Contribution

A novel deep learning model for automated sleep staging in rats using single-channel EEG data is introduced.

## Key findings

- The deep learning model achieved an average F1 score of 87.6% in classifying three sleep stages in rats.
- The model accurately predicted sleep architecture parameters like total and average bout durations.
- The tool generalizes well across different datasets and reduces the need for manual sleep staging.

## Abstract

Poor quality and poor duration of sleep have been associated with cognitive decline, diseases, and disorders. Therefore, sleep studies are imperative to recapitulate phenotypes associated with poor sleep quality and uncover mechanisms contributing to psychopathology. Classification of sleep stages, vigilance state bout durations, and number of transitions amongst vigilance states serves as a proxy for evaluating sleep quality in preclinical studies. Currently, the gold standard for sleep staging is expert human inspection of polysomnography (PSG) obtained from preclinical rodent models and this approach is immensely time consuming. To accelerate the analysis, we developed a deep-learning-based software tool for automated sleep stage classification in rats. This study aimed to develop an automated method for classifying three sleep stages in rats (REM/paradoxical sleep, NREM/slow-wave sleep, and wakefulness) using a deep learning approach based on single-channel EEG data. Single-channel EEG data were acquired from 16 rats, each undergoing two 24 h recording sessions. The data were labeled by human experts in 10 s epochs corresponding to three stages: REM/paradoxical sleep, NREM/slow-wave sleep, and wakefulness. A deep neural network (DNN) model was designed and trained to classify these stages using the raw temporal data from the EEG. The DNN achieved strong performance in predicting the three sleep stages, with an average F1 score of 87.6% over a cross-validated test set. The algorithm was able to predict key parameters of sleep architecture, including total bout duration, average bout duration, and number of bouts, with significant accuracy. Our deep learning model effectively automates the classification of sleep stages using single-channel EEG data in rats, reducing the need for labor-intensive manual annotation. This tool enables high-throughput sleep studies and may accelerate research into sleep-related pathologies. Furthermore, we provide over 700 h of expert-scored sleep data, available for public use in future research studies.

Our study presents a new deep learning model for automatically analyzing sleep patterns in rats using EEG data. This model was trained on one dataset and tested on two others, showing it can adapt to different data, which highlights its generalizability. This advancement could streamline sleep research by reducing manual scoring, making it faster and more consistent, thus aiding in studies of sleep disorders and drug effects on sleep in rodents.