Title: Anomaly detection for dual-channel sleep EEG signal with Mahalanobis-Taguchi-Gram-Schmidt metric

Authors: Jiufu Liu; Rui Zheng; Zaihong Zhou; Zhong Yang; Zhisheng Wang

Addresses: College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ' College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ' Department of Information Engineering, Guangdong Medical University, Dongguan 523808, China ' College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ' College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Abstract: To realise the automatic and accurate detection of human sleep quality and to overcome the complex and complicated process caused by artificial subjective discrimination, this paper presents a measurement algorithm of sleep EEG signals based on Mahalanobis-Taguchi-Gram-Schmidt model. The characteristic vectors of each channel are normalised in different staging segments, and the Schmidt orthogonal vector group of the linear independent vectors is obtained. The signal-to-noise ratio mean value of EEG in six-state sleep stages in 30-minute periods and 15-minute periods are calculated respectively with Mahalanobis-Taguchi-Gram-Schmidt method. The measurements of six-state sleep stages are analysed to identify and determine the normal and anomaly sleep quality. Mahalanobis-Taguchi-Gram-Schmidt metric for sleep EEG signals is effective in the detection of human sleep quality.

Keywords: anomaly detection; Mahalanobis-Taguchi system; sleep stages; signal to noise ratio.

DOI: 10.1504/IJRIS.2024.143166

International Journal of Reasoning-based Intelligent Systems, 2024 Vol.16 No.5, pp.337 - 344

Accepted: 10 Feb 2023
Published online: 05 Dec 2024 *

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