Detecting sleep spindles in EEGs using wavelet fourier analysis and statistical features

Al-Salman, Wessam and Li, Yan and Wen, Peng (2019) Detecting sleep spindles in EEGs using wavelet fourier analysis and statistical features. Biomedical Signal Processing and Control, 48. pp. 80-92. ISSN 1746-8094

Abstract

One of the more difficult tasks in sleep stage scoring is the detection of sleep spindles. Developing an effective method to identify these transitions in sleep electroencephalogram (EEG) recordings is an ongoing challenge, as there are typically hundreds of such transitions in each recording. This paper proposes a statistical model and a method based on wavelet Fourier analysis to detect sleep spindles. In this work, spindle detection is achieved in two phases: a training phase and a testing phase. An EEG signal is first divided into segments, using a sliding window technique. The size of the window is 0.5 s, with an overlap of 0.4 s. Then, each EEG segment is decomposed using a discrete wavelet transform into different levels of decompositions. The wavelet detail coefficient at level 3 (D3) is selected from these parameters, and this is passed through a fast Fourier transform to identify the desired frequency bands {α, β, θ, δ, γ}. Ten statistical characteristics are extracted from each band. Nonparametric Kruskal-Wallis one-way analysis of variance is used to select the important features, representing each of the 0.5 s EEG segments. To detect all possible occurrences of sleep spindles in the original EEG signals, four different window sizes of 0.25, 1.0, 1.5 and 2.0 s are also tested. Finally, the extracted features are used as the input to four classifiers to detect the sleep spindles: a least-squares support vector machine (LS-SVM), K-nearest neighbours, a K-means algorithm and a C4.5 decision tree. The obtained results demonstrate that the proposed method yields optimal results with a window size of 0.5 s. The maximum averages of accuracy, sensitivity and specificity are 97.9%, 98.5% and 97.8%, respectively. This method can efficiently detect spindles in EEG signals, and can assist sleep experts in analysing EEG signals.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions. ©Elsevier Ltd.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 01 Feb 2019 05:00
Last Modified: 28 Feb 2019 01:32
Uncontrolled Keywords: sleep spindles; statistical model; wavelet fourier analysis EEG signals
Fields of Research : 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 > 080199 Artificial Intelligence and Image Processing not elsewhere classified
09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
09 Engineering > 0903 Biomedical Engineering > 090399 Biomedical Engineering not elsewhere classified
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970110 Expanding Knowledge in Technology
E Expanding Knowledge > 97 Expanding Knowledge > 970111 Expanding Knowledge in the Medical and Health Sciences
Identification Number or DOI: 10.1016/j.bspc.2018.10.004
URI: http://eprints.usq.edu.au/id/eprint/35169

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