Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification
المؤلفون المشاركون
Wang, Yuanfa
Li, Zunchao
Feng, Lichen
Zheng, Chuang
Zhang, Wenhao
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-06-19
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice.
This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection.
Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands.
Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector.
After comparing five multiclass SELM strategies, the one-against-one strategy with the highest accuracy is chosen for the three-class classification system.
The performance of the designed three-class classification system is tested with publicly available epilepsy dataset.
The results show that the system achieves high enough classification accuracy by combining the SELM and DWT and reduces training and testing time by decreasing computational complexity and feature dimension.
With excellent classification performance and low computation complexity, this three-class classification system can be utilized for practical epileptic EEG detection, and it offers great potentials for portable automatic epilepsy and seizure detection system in the future hardware implementation.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Yuanfa& Li, Zunchao& Feng, Lichen& Zheng, Chuang& Zhang, Wenhao. 2017. Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142264
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Yuanfa…[et al.]. Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1142264
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Yuanfa& Li, Zunchao& Feng, Lichen& Zheng, Chuang& Zhang, Wenhao. Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142264
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142264
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