An efficient approach for EEG sleep spindles detection based on fractal dimension coupled with time frequency image

Al-Salman, Wessam and Li, Yan and Wen, Peng and Diykh, Mohammed (2018) An efficient approach for EEG sleep spindles detection based on fractal dimension coupled with time frequency image. Biomedical Signal Processing and Control, 41. pp. 210-221. ISSN 1746-8094

Abstract

Detection of the characteristics of the sleep stages, such as sleep spindles and K-complexes in EEG signals,is a challenging task in sleep research as visually detecting them requires high skills and efforts from sleep experts. In this paper, we propose a robust method based on time frequency image (TFI) and fractal dimension (FD) to detect sleep spindles in EEG signals. The EEG signals are divided into segments using a sliding window technique. The window size is set to 0.5 s with an overlapping of 0.4 s. A short time Fourier transform (STFT) is applied to obtain a TFI from each EEG segment. Each TFI is converted into an 8-bitbinary image. Then, a box counting method is applied to estimate and discover the FDs of EEG signals.Different sets of features are extracted from each TFI after applying a statistical model to the FD of each TFI.The extracted statistical features are fed to a least square support vector machine (LS SVM) to figure out the best combination of the features. As a result, the proposed method is found to have a high classification rate with the eight features sets. To verify the effectiveness of the proposed method, different classifiers,including a K-means, Naive Bayes and a neural network, are also employed. In this paper, the proposed method is evaluated using two publically available datasets: Dream sleep spindles and Montreal archive of sleep studies. The proposed method is compared with the current existing methods, and the results revealed that the proposed method outperformed the others. An average accuracy of 98.6% and 97.1% is obtained by the proposed method for the two datasets, respectively.


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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 04:51
Last Modified: 28 Feb 2019 01:36
Uncontrolled Keywords: sleep spindles; time frequency image; fractal dimension; box counting; EEG signals
Fields of Research : 08 Information and Computing Sciences > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified
01 Mathematical Sciences > 0104 Statistics > 010499 Statistics not elsewhere classified
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
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.2017.11.019
URI: http://eprints.usq.edu.au/id/eprint/34850

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