Abstract
Primary insomnia is a term used to describe a subtype of insomnia that constitutes the disorder itself and is not a consequent to any other psychiatric or sleep disorder. Hitherto, there is no clear objective markers from Polysomnography (PSG) signal to characterize insomnia. Although linear methods like spectral analysis of EEG frequency bands have been used to detect physiological arousal in patients with insomnia, these methods may not be sufficient enough to extract valuable information and detect abnormalities in the signals. The EEG signal itself originate from a complex neuronal activity in the brain, therefore the use of nonlinear measures may show some hidden information that could better explain the activation of this hyperarousal. The aim of the present study is to classify the primary insomnia patient from the healthy based on the supervised learning machine technique of SVM and the usage of nonlinear features of EEG signal. The classification result by using SVM achieved an overall of 83% of accuracy, 85 and 80% of sensitivity and specificity respectively.
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Acknowledgements
This research was funded under Research University Grant (Vot No. 15H54) from Universiti Teknologi Malaysia. The authors would like to thank the Research Management Centre of UTM and the Ministry of Education Malaysia for their financial support.
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Abdullah, H., Patti, C.R., Dissanyaka, C., Penzel, T., Cvetkovic, D. (2018). Support Vector Machine Classification of EEG Nonlinear Features for Primary Insomnia. In: Ibrahim, F., Usman, J., Ahmad, M., Hamzah, N., Teh, S. (eds) 2nd International Conference for Innovation in Biomedical Engineering and Life Sciences. ICIBEL 2017. IFMBE Proceedings, vol 67. Springer, Singapore. https://doi.org/10.1007/978-981-10-7554-4_28
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DOI: https://doi.org/10.1007/978-981-10-7554-4_28
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