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Automated classification of multi-class sleep stages classification using polysomnography signals: a nine- layer 1D-convolution neural network approach

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Abstract

Sleep disorder diseases have one of the major health issues across the world. To handle this issue the primary step taken by most of the sleep experts is the sleep staging classification. The whole visual inspection process is carried out manually by the sleep experts, which can be a highly time-consumed task and creates a lot of annotation errors due to more human interventions. In this study, we introduce an efficient and robust approach to improve the sleep staging accuracy. In this paper, we proposed an automated deep nine-layer one-dimensional convolution neural network for multi-class sleep staging classification (9 L-1D-CNN-SSC) using polysomnography (PSG) signals. The proposed 9 L-1D-CNN-SSC model comprises eleven layers with learnable parameters: nine convolution layers and two fully connected layers. The main objective of designing such a model is to achieve higher classification accuracy for multiclass sleep stages classifications with reduced learnable parameters. The proposed network architecture is tested on two different subgroups recordings of ISRUC-Sleep datasets namely ISRUC-Sleep subgroup1 (ISR-SG-I), and ISRUC-Sleep subgroup3 (ISR-SG-III). The proposed model is compiled with eight different individual experiments based on a single-channel electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and combinations of EEG + EOG+ EMG signals. The proposed 9 L-1D-CNN-SSC model achieved the highest classification accuracy of 99.03%, 99.50%, and 99.03% for three to five sleep stages classification, respectively with single-channel of EEG signals, similarly, the model achieved 98.93% for two-state sleep stage classification with EMG signals using the ISR-SG-I dataset. The same model achieved the highest classification accuracy of 98.88%, 98.76%, and 98.67% for three-five sleep stages classification with a single-channel EMG signal, and 99.24% for two-state sleep classification with single-channel EOG using ISR-SG-III dataset. It has been observed that the obtained results from the proposed 9 L-1D-CNN-SSC model give the best classification accuracy performance on multiclass sleep stages classification incomparable to the existing literature works. The developed 9 L-1D-CNN-SSC deep learning architecture is ready for clinical usage with high PSG data.

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Data availability

All EEG files are available from the ISRUC-SLEEP database (https://sleeptight.isr.uc.pt/ISRUC_Sleep/).

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Authors and Affiliations

  1. Puducherry Technological University, Puducherry, India

    Santosh Kumar Satapathy & D Loganathan

  2. Pandit Deendayal Energy University, Gandhinagar, India

    Santosh Kumar Satapathy

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  1. Santosh Kumar Satapathy
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  2. D Loganathan
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Correspondence to Santosh Kumar Satapathy.

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Satapathy, S.K., Loganathan, D. Automated classification of multi-class sleep stages classification using polysomnography signals: a nine- layer 1D-convolution neural network approach. Multimed Tools Appl 82, 8049–8091 (2023). https://doi.org/10.1007/s11042-022-13195-2

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  • DOI: https://doi.org/10.1007/s11042-022-13195-2

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