EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis
المؤلفون المشاركون
Zhang, Bingtao
Lei, Tao
Liu, Hong
Cai, Hanshu
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-09-04
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers.
Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed.
Therefore, automatic sleep staging is essential in order to solve these problems.
In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed.
Firstly, EEG data are preprocessed to remove artifacts, extract features, and normalization.
Secondly, the normalized features and other context information are stored using an ontology-based model (OBM).
Thirdly, an improved method of self-adaptive correlation analysis is designed to select the most effective EEG features.
Based on these EEG features and weighting features analysis, the improved random forest (RF) is considered as the classifier to achieve the classification of sleep stages.
To investigate the classification ability of the proposed method, several sets of experiments are designed and conducted to classify the sleep stages into two, three, four, and five states.
The accuracy of five-state classification is 89.37%, which is improved compared to the accuracy using unimproved RF (84.37%) or previously reported classifiers.
In addition, a set of controlled experiments is executed to verify the effect of the number of sleep segments (epochs) on the classification, and the results demonstrate that the proposed scheme is less affected by the sleep segments.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zhang, Bingtao& Lei, Tao& Liu, Hong& Cai, Hanshu. 2018. EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis. Computational and Mathematical Methods in Medicine،Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1132095
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zhang, Bingtao…[et al.]. EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis. Computational and Mathematical Methods in Medicine No. 2018 (2018), pp.1-16.
https://search.emarefa.net/detail/BIM-1132095
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zhang, Bingtao& Lei, Tao& Liu, Hong& Cai, Hanshu. EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis. Computational and Mathematical Methods in Medicine. 2018. Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1132095
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1132095
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