Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. With...

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Bibliographic Details
Published in:Applied sciences 2020-11, Vol.10 (21), p.7639
Main Authors: Hasan, Md Junayed, Shon, Dongkoo, Im, Kichang, Choi, Hyun-Kyun, Yoo, Dae-Seung, Kim, Jong-Myon
Format: Article
Language:English
Subjects:
Brain
Classification
Deep learning
EEG
EEG signals
Electrodes
Electroencephalography
Eye movements
Gender differences
machine learning
Neural networks
Occipital lobes
Signal processing
Sleep
Sleep disorders
sleep stage
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Summary:This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. Without considering any artifact removal approach, the residual neural network (ResNet) architecture is used to automatically learn the distinctive features of different sleep stages from the power spectral density (PSD) of the raw EEG data. The residual block of the ResNet learns the intrinsic features of different sleep stages from the EEG data while avoiding the vanishing gradient problem. The proposed approach is validated using the sleep dataset of the Dreams database, which comprises of EEG signals for 20 healthy human subjects, 16 female and 4 male. Our experimental results demonstrate the effectiveness of the ResNet based approach in identifying different sleep stages in both female and male subjects compared to state-of-the-art methods with classification accuracies of 87.8% and 83.7%, respectively.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10217639