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Automated analysis of sleep study parameters using signal processing and artificial intelligence.

Sohaib, Muhammad; Ghaffar, Ayesha; Shin, Jungpil; Hasan, Md Junayed; Suleman, Muhammad Taseer

Authors

Muhammad Sohaib

Ayesha Ghaffar

Jungpil Shin

Muhammad Taseer Suleman



Abstract

An automated sleep stage categorization can readily face noise-contaminated EEG recordings, just as other signal processing applications. Therefore, the denoising of the contaminated signals is inevitable to ensure a reliable analysis of the EEG signals. In this research work, an empirical mode decomposition is used in combination with stacked autoencoders to conduct automatic sleep stage classification with reliable analytical performance. Due to the decomposition of the composite signal into several intrinsic mode functions, empirical mode decomposition offers an effective solution for denoising non-stationary signals such as EEG. Preliminary results showed that through these intrinsic modes, a signal with a high signal-to-noise ratio can be obtained, which can be used for further analysis with confidence. Therefore, later, when statistical features were extracted from the denoised signals and were classified using stacked autoencoders, improved results were obtained for Stage 1, Stage 2, Stage 3, Stage 4, and REM stage EEG signals using this combination.

Citation

SOHAIB, M., GHAFFAR, A., SHIN, J., HASAN. M.J. and SULEMAN, M.T. 2022. Automated analysis of sleep study parameters using signal processing and artificial intelligence. International journal of environmental research and public health [online], 19(20), article number 13256. Available from: https://doi.org/10.3390/ijerph192013256

Journal Article Type Article
Acceptance Date Oct 12, 2022
Online Publication Date Oct 14, 2022
Publication Date Oct 31, 2022
Deposit Date Oct 20, 2022
Publicly Available Date Nov 8, 2022
Journal International Journal of Environmental Research and Public Health
Print ISSN 1661-7827
Electronic ISSN 1660-4601
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 19
Issue 20
Article Number 13256
Series Title Role of data science, and computer vision in public health
DOI https://doi.org/10.3390/ijerph192013256
Keywords Autoencoders; Biomedical signals; Deep learning; EEG signals; Sleep study; Sleep stage classification
Public URL https://rgu-repository.worktribe.com/output/1783425

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