Characterization of fibromyalgia using sleep EEG signals with nonlinear dynamical features
| Autor: | Jose Kunnel Paul, Yuki Hagiwara, Thomas Iype, Joel E.W. Koh, U. Rajendra Acharya, Dileep R |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Adult Male Fibromyalgia Support Vector Machine Computer science Health Informatics Lyapunov exponent Electroencephalography 03 medical and health sciences symbols.namesake 0302 clinical medicine medicine Humans Hurst exponent Sleep Stages medicine.diagnostic_test Kolmogorov complexity business.industry Pattern recognition Signal Processing Computer-Assisted Middle Aged medicine.disease Computer Science Applications Sample entropy Support vector machine 030104 developmental biology Nonlinear Dynamics symbols Female Artificial intelligence business Sleep 030217 neurology & neurosurgery |
| Zdroj: | Computers in biology and medicine. 111 |
| ISSN: | 1879-0534 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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