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Characterization of fibromyalgia using sleep EEG signals with nonlinear dynamical features.
Comput Biol Med. 2019 08; 111:103331.CB

Abstract

Fibromyalgia is an intense musculoskeletal pain causing sleep, fatigue, and mood problems. Sleep studies have suggested that 70%-80% of fibromyalgia patients complain of non-restorative sleep. The abnormalities in sleep have been implicated as both a cause and effect of the disease. In this paper, the electroencephalogram (EEG) signals of sleep stages 2 and 3 are used to classify the normal and fibromyalgia classes automatically. We have used various nonlinear parameters, namely sample entropy (SampEn), fractal dimension (FD), higher order spectra (HOS), largest Lyapunov exponent (LLE), Kolmogorov complexity (KC), Hurst exponent (HE), energy, and power in various frequency bands from the EEG signals. Then these features are subjected to Student's t-test to select the clinically significant features, and are classified using the support vector machine (SVM) classifier. Our proposed method can classify normal and fibromyalgia subjects using the stage 2 sleep EEG signals with an accuracy of 96.15%, sensitivity and specificity of 96.88% and 95.65%, respectively. Performance of the developed system can be improved further by adding more subjects in each class, and can be employed for clinical use.

Authors+Show Affiliations

Department of Neurology, Government Medical College, Thiruvananthapuram, Kerala, India.Department of Neurology, Government Medical College, Thiruvananthapuram, Kerala, India.Department of Neurology, Government Medical College, Thiruvananthapuram, Kerala, India.Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500, Subang Jaya, Malaysia. Electronic address: aru@np.edu.sg.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31284155

Citation

Paul, Jose Kunnel, et al. "Characterization of Fibromyalgia Using Sleep EEG Signals With Nonlinear Dynamical Features." Computers in Biology and Medicine, vol. 111, 2019, p. 103331.
Paul JK, Iype T, R D, et al. Characterization of fibromyalgia using sleep EEG signals with nonlinear dynamical features. Comput Biol Med. 2019;111:103331.
Paul, J. K., Iype, T., R, D., Hagiwara, Y., Koh, J. W., & Acharya, U. R. (2019). Characterization of fibromyalgia using sleep EEG signals with nonlinear dynamical features. Computers in Biology and Medicine, 111, 103331. https://doi.org/10.1016/j.compbiomed.2019.103331
Paul JK, et al. Characterization of Fibromyalgia Using Sleep EEG Signals With Nonlinear Dynamical Features. Comput Biol Med. 2019;111:103331. PubMed PMID: 31284155.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Characterization of fibromyalgia using sleep EEG signals with nonlinear dynamical features. AU - Paul,Jose Kunnel, AU - Iype,Thomas, AU - R,Dileep, AU - Hagiwara,Yuki, AU - Koh,JoelE W, AU - Acharya,U Rajendra, Y1 - 2019/06/18/ PY - 2019/04/10/received PY - 2019/06/16/revised PY - 2019/06/17/accepted PY - 2019/7/10/pubmed PY - 2019/7/10/medline PY - 2019/7/9/entrez KW - Classifier KW - EEG KW - Fibromyalgia KW - Nonlinear KW - SVM KW - Sleep SP - 103331 EP - 103331 JF - Computers in biology and medicine JO - Comput. Biol. Med. VL - 111 N2 - Fibromyalgia is an intense musculoskeletal pain causing sleep, fatigue, and mood problems. Sleep studies have suggested that 70%-80% of fibromyalgia patients complain of non-restorative sleep. The abnormalities in sleep have been implicated as both a cause and effect of the disease. In this paper, the electroencephalogram (EEG) signals of sleep stages 2 and 3 are used to classify the normal and fibromyalgia classes automatically. We have used various nonlinear parameters, namely sample entropy (SampEn), fractal dimension (FD), higher order spectra (HOS), largest Lyapunov exponent (LLE), Kolmogorov complexity (KC), Hurst exponent (HE), energy, and power in various frequency bands from the EEG signals. Then these features are subjected to Student's t-test to select the clinically significant features, and are classified using the support vector machine (SVM) classifier. Our proposed method can classify normal and fibromyalgia subjects using the stage 2 sleep EEG signals with an accuracy of 96.15%, sensitivity and specificity of 96.88% and 95.65%, respectively. Performance of the developed system can be improved further by adding more subjects in each class, and can be employed for clinical use. SN - 1879-0534 UR - https://www.unboundmedicine.com/medline/citation/31284155/Characterization_of_fibromyalgia_using_sleep_EEG_signals_with_nonlinear_dynamical_features L2 - https://linkinghub.elsevier.com/retrieve/pii/S0010-4825(19)30200-8 DB - PRIME DP - Unbound Medicine ER -
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