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

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

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