Classification of Resting-State Status Based on Sample Entropy and Power Spectrum of Electroencephalography (EEG)
المؤلفون المشاركون
Duru, Adil Deniz
Mohamed, Ahmed M. A.
Uçan, Osman N.
Bayat, Oğuz
المصدر
Applied Bionics and Biomechanics
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-11
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
An electroencephalogram (EEG) is a significant source of diagnosing brain issues.
It is also a mediator between the external world and the brain, especially in the case of any mental illness; however, it has been widely used to monitor the dynamics of the brain in healthy subjects.
This paper discusses the resting state of the brain with eyes open (EO) and eyes closed (EC) by using sixteen channels by the use of conventional frequency bands and entropy of the EEG signal.
The Fast Fourier Transform (FFT) and sample entropy (SE) of each sensor are computed as methods of feature extraction.
Six classifiers, including logistic regression (LR), K-Nearest Neighbors (KNN), linear discriminant (LD), decision tree (DT), support vector machine (SVM), and Gaussian Naive Bayes (GNB) are used to discriminate the resting states of the brain based on the extracted features.
EEG data were epoched with one-second-length windows, and they were used to compute the features to classify EO and EC conditions.
Results showed that the LR and SVM classifiers had the highest average classification accuracy (97%).
Accuracies of LD, KNN, and DT were 95%, 93%, and 92%, respectively.
GNB gained the least accuracy (86%) when conventional frequency bands were used.
On the other hand, when SE was used, the average accuracies of SVM, LD, LR, GNB, KNN, and DT algorithms were 92% 90%, 89%, 89%, 86%, and 86%, respectively.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Mohamed, Ahmed M. A.& Uçan, Osman N.& Bayat, Oğuz& Duru, Adil Deniz. 2020. Classification of Resting-State Status Based on Sample Entropy and Power Spectrum of Electroencephalography (EEG). Applied Bionics and Biomechanics،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1120194
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Mohamed, Ahmed M. A.…[et al.]. Classification of Resting-State Status Based on Sample Entropy and Power Spectrum of Electroencephalography (EEG). Applied Bionics and Biomechanics No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1120194
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Mohamed, Ahmed M. A.& Uçan, Osman N.& Bayat, Oğuz& Duru, Adil Deniz. Classification of Resting-State Status Based on Sample Entropy and Power Spectrum of Electroencephalography (EEG). Applied Bionics and Biomechanics. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1120194
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1120194
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر