Sleep Stage Classification Using a Pre-trained Deep Learning Model
This work addresses sleep disorder diagnosis by improving classification accuracy, but it is incremental as it builds on pre-trained models for a specific domain.
The researchers tackled the problem of sleep stage classification for diagnosing sleep disorders by developing a machine-learning model called EEGMobile, which achieved an accuracy of 86.97% on the Sleep-EDF20 dataset and 56.4% in stage N1, outperforming other models.
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.