Sleep Stage Classification Using a Pre-trained Deep Learning Model

• URL: http://arxiv.org/abs/2309.07182v2
• Date: Fri, 6 Oct 2023 13:18:32 GMT
• Title: Sleep Stage Classification Using a Pre-trained Deep Learning Model
• Authors: Hassan Ardeshir, Mohammad Araghi
• Abstract summary: "EEGMobile" is a machine-learning model that 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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