دانشگاه سمنان

 آمار

تعداد نشریات21
تعداد شماره‌ها671
تعداد مقالات9,774
تعداد مشاهده مقاله69,546,452
تعداد دریافت فایل اصل مقاله48,663,153
Hussein, Hussein, Abdalla, Kasim. (1401). Seizure prediction algorithm based on simulated annealing and machine learning. نشریه هنرهای کاربردی, 14(1), 1499-1508. doi: 10.22075/ijnaa.2022.7154
Hussein M. Hussein; Kasim K. Abdalla. "Seizure prediction algorithm based on simulated annealing and machine learning". نشریه هنرهای کاربردی, 14, 1, 1401, 1499-1508. doi: 10.22075/ijnaa.2022.7154
Hussein, Hussein, Abdalla, Kasim. (1401). 'Seizure prediction algorithm based on simulated annealing and machine learning', نشریه هنرهای کاربردی, 14(1), pp. 1499-1508. doi: 10.22075/ijnaa.2022.7154
Hussein, Hussein, Abdalla, Kasim. Seizure prediction algorithm based on simulated annealing and machine learning. نشریه هنرهای کاربردی, 1401; 14(1): 1499-1508. doi: 10.22075/ijnaa.2022.7154

Seizure prediction algorithm based on simulated annealing and machine learning

International Journal of Nonlinear Analysis and Applications
مقاله 114، دوره 14، شماره 1، فروردین 2023، صفحه 1499-1508    اصل مقاله (428 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.7154
نویسندگان
Hussein M. Hussein1؛ Kasim K. Abdalla* 2
1Ninevah University, Mosul, Iraq
2University of Babylon, Hilla, Iraq
تاریخ دریافت: 23 شهریور 1401،  تاریخ بازنگری: 03 آبان 1401،  تاریخ پذیرش: 29 آبان 1401 
چکیده
Epilepsy is one of the widespread diseases of the central nervous system around the world, which is characterized by seizures with different periods and symptoms among people. Expecting incoming seizures is important and necessary to organize the patient's life and take the necessary precautions to preserve his life. Methods: We used a seizure prediction algorithm based on four stages. In the first round, one of the person's Electroencephalogram (EEG) channels is selected to be the input channel based on a statistical analysis of the person's EEG recordings. In the second round, the preictal period length (PIL) and the length of the sample segment (SEG) used are calculated. In the third round, several features from the selected channel’s data are extracted, while in the fourth round, the simulated annealing method is used to find a set of features that achieve the best performance in differentiating between the Preictal and Interictal periods. Results: The results showed an average sensitivity of 77\% and a low false prediction rate of 0.2 by testing the algorithm with the CHB-MIT scalp dataset. Conclusion and Significance: Comparing our results with the results of recent research showed the superiority of our results in terms of using one channel compared to multiple channels in other research.
کلیدواژه‌ها
Biomedical and Health Informatics؛ Feature Extraction؛ Machine Learning؛ Seizure prediction؛ Simulated Annealing
آمار
تعداد مشاهده مقاله: 17,373
تعداد دریافت فایل اصل مقاله: 9,120


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب