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Abdulbaqi, Azmi, Younis, Muhanad, Younus, Younus, Obaid, Ahmed. (1400). A hybrid technique for EEG signals evaluation and classification as a step towards to neurological and cerebral disorders diagnosis. نشریه هنرهای کاربردی, 13(1), 773-781. doi: 10.22075/ijnaa.2022.5590
Azmi Shawkat Abdulbaqi; Muhanad Tahrir Younis; Younus Tahreer Younus; Ahmed J Obaid. "A hybrid technique for EEG signals evaluation and classification as a step towards to neurological and cerebral disorders diagnosis". نشریه هنرهای کاربردی, 13, 1, 1400, 773-781. doi: 10.22075/ijnaa.2022.5590
Abdulbaqi, Azmi, Younis, Muhanad, Younus, Younus, Obaid, Ahmed. (1400). 'A hybrid technique for EEG signals evaluation and classification as a step towards to neurological and cerebral disorders diagnosis', نشریه هنرهای کاربردی, 13(1), pp. 773-781. doi: 10.22075/ijnaa.2022.5590
Abdulbaqi, Azmi, Younis, Muhanad, Younus, Younus, Obaid, Ahmed. A hybrid technique for EEG signals evaluation and classification as a step towards to neurological and cerebral disorders diagnosis. نشریه هنرهای کاربردی, 1400; 13(1): 773-781. doi: 10.22075/ijnaa.2022.5590

A hybrid technique for EEG signals evaluation and classification as a step towards to neurological and cerebral disorders diagnosis

International Journal of Nonlinear Analysis and Applications
مقاله 63، دوره 13، شماره 1، خرداد 2022، صفحه 773-781    اصل مقاله (549.76 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.5590
نویسندگان
Azmi Shawkat Abdulbaqi1؛ Muhanad Tahrir Younis* 2؛ Younus Tahreer Younus3؛ Ahmed J Obaid4
1College of Computer Science and Information Technology, University of Anbar, Iraq
2Department of Computer Science, Science College, Mustansiriyah University, Baghdad, Iraq.
3Department of Oil and gas economics, college of administration sciences and financial, Imam Ja’afar Al-sadiq university, Baghdad, Iraq
4Faculty of Computer Science and Mathematics, University of Kufa, Iraq.
تاریخ دریافت: 09 تیر 1400،  تاریخ پذیرش: 02 مهر 1400 
چکیده
Electroencephalography (EEG) signals are commonly used to identify and diagnose brain disorders. Each EEG normal waveform consists of the following waveforms: Gamma(γ) wave, Beta (β) wave, Alpha (α), Theta (θ), and Delta (δ). The term Neurological Diseases ” NurDis ” is used to describe a variety of conditions that affect the nervous Epilepsy, neuro infections (bacterial and viral), brain tumors, cerebrovascular diseases, Alzheimer’s disease, and various dementias are all examples of neurological disorders. Encephalitis is one of the illnesses that affects the brain. The EEG signals used in this paper were from the CHB-MIT Scalp EEG database. The discrete wavelet transform (DWT) was utilized to extract characteristics from the filtered EEG data. Finally, classifiers such as K Nearest Neighbor (KNN) and Support vector machine (SVM) were used to categorize the EEG signals into normal and pathological signal classes using all of the computed characteristics. In order to categorize the signal in a normal and anomalous group, the KNN and SVM classifiers are employed. For both classifiers, performance assessments (accuracy, sensitivity and specificity) are determined. KNN classifier accuracy is 71.88%, whereas SVM classifier accuracy is 81.23%. The sensitivity of KNN and SVM are 80.14% and 77.31%, respectively. The KNN classification specificity is 69.62% and the SVM classification specificity is 98%. Both classifiers performance is evaluated using the confusion matrix.
کلیدواژه‌ها
Discrete wavelet transform (DWT)؛ Support vector machine (SVM)؛ Electroencephalography (EEG) signals؛ K-Nearest neighbor (KNN)
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