Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier

Al-Salman, Wessam and Li, Yan and Wen, Peng (2021) Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier. Neuroscience Research, 172. pp. 26-40. ISSN 0168-0102


Abstract

Sleep scoring is one of the primary tasks for the classification of sleep stages using electroencephalogram(EEG) signals. It is one of the most important diagnostic methods in sleep research and must be carried out with a high degree of accuracy because any errors in the scoring in the patient’s sleep EEG recordings an cause serious problems. The aim of this research is to develop a new automatic method for detecting the most important characteristics in sleep stage 2 such as k-complexes based on multi-domain features. In this study, each EEG signal is divided into a set of segments using a sliding window technique. Based on extensive experiments during the training phase, the size of the sliding window is set to 0.5 s (s). Then a set of statistical, fractal, frequency and non-linear features are extracted from each epoch based on the time domain, Katz’s algorithm, power spectrum density (PSD) and tunable Q-factor wavelet transform(TQWT). As a result, a vector of twenty-two features is obtained to represent each EEG segment. In order to detect k-complexes, the extracted features were analysed for their ability to detect the k-complex waveforms. Based on the analysis of the features, twelve out of twenty-two features are selected and forwarded to a least square support vector machine (LS-SVM) classifier to identify k-complexes in EEG signals. A set of various classification techniques of K-means and extreme learning machine classifiers are used to compare the obtained results and to evaluate the performance of the proposed method. The experimental results showed that the proposed method, based on multi-domain features, achieved better recognition results than other methods and classifiers. An average accuracy, sensitivity and specificity of97.7 %, 97 %, and 94.2 % were obtained, respectively, with the CZ-A1 channel according to the R&K standard. The experimental results with high classification performance demonstrated that the technique can help doctors optimize the diagnosis and treatment of sleep disorders.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online: 11 May 2021. Permanent restricted access to ArticleFirst version, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Date Deposited: 07 Jul 2021 02:56
Last Modified: 20 Sep 2021 05:10
Uncontrolled Keywords: EEG signal, K-complexes detection, tunable Q-factor wavelet transform(TQWT) power spectrum density (PSD), Katz’s algorithm Fractal, statistical features, LS-SVM classifier
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461106 Semi- and unsupervised learning
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970110 Expanding Knowledge in Technology
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280103 Expanding knowledge in the biomedical and clinical sciences
Identification Number or DOI: https://doi.org/10.1016/j.neures.2021.03.012
URI: http://eprints.usq.edu.au/id/eprint/42638

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