چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده ثبت کننده مقاله | مهداد اسمعیلی |
| مرحله جاری مقاله | تایید نهایی |
| دانشکده/مرکز مربوطه | دانشکده علوم نوین پزشکی |
| کد مقاله | 76177 |
| عنوان فارسی مقاله | An strong approach to detect sleep-deprived brain based on EEG signals |
| عنوان لاتین مقاله | An strong approach to detect sleep-deprived brain based on EEG signals |
| نوع ارائه | پوستر |
| عنوان کنگره / همایش | Sharif Neuroscience Symposium 2021 |
| نوع کنگره / همایش | بین المللی |
| کشور محل برگزاری کنگره/ همایش | |
| شهر محل برگزاری کنگره/ همایش | تهران |
| سال انتشار/ ارائه شمسی | 1399 |
| سال انتشار/ارائه میلادی | 2021 |
| تاریخ شمسی شروع و خاتمه کنگره/همایش | 1399/12/13 الی 1399/12/15 |
| آدرس لینک مقاله/ همایش در شبکه اینترنت | http://sns.ee.sharif.ir/wp-content/uploads/2021/02/SNS2021Booklet.v1-2.pdf |
| آدرس علمی (Affiliation) نویسنده متقاضی | Tabriz University of Medical sciences |
| نویسنده | نفر چندم مقاله |
|---|---|
| علی فخاری دهخوارقانی | سوم |
| سعید چارسوئی | چهارم |
| علی احمدعلی پور | هفدهم |
| محمدرضا صدقی گمچی | اول |
| مهداد اسمعیلی | دوم |
| امیرمحمد شرفی | دوازدهم |
| عنوان | متن |
|---|---|
| خلاصه مقاله | Sleep deprivation commonly results in an alteration of the brain and cognitive functions which are related to many potentially dangerous outcomes. Current clinical assessment methods only modestly distinguish sleep-deprived fatigued brains using the results of cognitive functions which are sometimes inconsistent. This paper, introduces a precise discriminative and robust approach to detect a sleepdeprived brain based on extracted biomarkers of a 2-dimensional discrete wavelet transform of the EEG signals. Cognitive performance and EEG signals were obtained from twenty-seven healthy participants (12 females; age range: 19-29 years old) after normal sleep and 24-hour sleep deprivation situations. A 2-D discrete wavelet transform coeffcients of closed eye epochs in the resting-state of EEG signal for both conditions were calculated and five features were extracted from the transformed domain coeffcients. Sleep-deprived and normal sleep EEG signals were precisely classified using SVM, ANFIS, and random forest classifiers, whereas random forest and SVM performs very close and better than ANFIS. Although mean differences of some cognitive tests were significantly different in the sleep deprivation night compared to the normal sleep night, the results of the classifiers of cognitive tests showed medium to the weak performance of these tests to detect sleep-deprived brain. The introduced EEG biomarkers exhibit very high predictive accuracy in comparison with traditionally used power spectrum density of EEG signals. |
| کلمات کلیدی |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| SNS2021Bookle.pdf | 1400/04/08 | 5375709 | دانلود |
| abstract.jpg | 1400/04/08 | 302649 | دانلود |
| Untitled.jpg | 1400/04/08 | 202632 | دانلود |
