Sleep Stage Classification Using a Pre-trained Deep Learning Model

TL;DR

This paper introduces EEGMobile, a pre-trained deep learning model that classifies sleep stages with high accuracy, especially improving detection of stage N1, aiding in sleep disorder diagnosis and treatment.

Contribution

The paper presents a novel pre-trained deep learning approach using EEG spectrograms that outperforms existing models in sleep stage classification, notably in accuracy for stage N1.

Findings

Achieved 86.97% accuracy on Sleep-EDF20 dataset

Recorded 56.4% accuracy in stage N1, surpassing other models

Demonstrated potential for improved sleep disorder diagnosis

Abstract

One of the common human diseases is sleep disorders. The classification of sleep stages plays a fundamental role in diagnosing sleep disorders, monitoring treatment effectiveness, and understanding the relationship between sleep stages and various health conditions. A precise and efficient classification of these stages can significantly enhance our understanding of sleep-related phenomena and ultimately lead to improved health outcomes and disease treatment. Models others propose are often time-consuming and lack sufficient accuracy, especially in stage N1. The main objective of this research is to present a machine-learning model called "EEGMobile". This model utilizes pre-trained models and learns from electroencephalogram (EEG) spectrograms of brain signals. The model achieved an accuracy of 86.97% on a publicly available dataset named "Sleep-EDF20", outperforming other models proposed by different researchers. Moreover, it recorded an accuracy of 56.4% in stage N1, which is better than other models. These findings demonstrate that this model has the potential to achieve better results for the treatment of this disease.