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

Overview

Journal Int J Environ Res Public Health

Publisher MDPI

Specialties Environmental Health
Public Health

Date 2022 Oct 27

PMID 36293844

Authors

Muhammad Sohaib

Ayesha Ghaffar

Jungpil Shin

Md Junayed Hasan

Muhammad Taseer Suleman

Affiliations

Soon will be listed here.

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.

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