Automated Analysis of Sleep Study Parameters Using Signal Processing and Artificial Intelligence

Muhammad Sohaib (), Ayesha Ghaffar, Jungpil Shin, Md Junayed Hasan () and Muhammad Taseer Suleman
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Muhammad Sohaib: Department of Software Engineering, Lahore Garrison University, Lahore 54000, Pakistan
Ayesha Ghaffar: Department of Software Engineering, Lahore Garrison University, Lahore 54000, Pakistan
Jungpil Shin: School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima, Japan
Md Junayed Hasan: National Subsea Centre, Robert Gordon University, Scotland AB10 7AQ, UK
Muhammad Taseer Suleman: Digital Forensics Research and Service Centre, Lahore Garrison University, Lahore 54000, Pakistan

IJERPH, 2022, vol. 19, issue 20, 1-11

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.

Keywords: autoencoders; biomedical signals; deep learning; EEG signals; sleep study; sleep stage classification (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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