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Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine
المؤلفون المشاركون
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-02-19
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems.
The early detection of PTB has great significance for its prevention.
The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB.
This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM).
For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD).
Then, the Hilbert transform was applied to IMF to obtain analytic function.
The maximum amplitude of analytic function was extracted as feature.
The identification model was constructed based on ELM.
Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%.
The area under receiver operating characteristic (ROC) curve is 0.88.
Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Chen, Lili& Hao, Yaru. 2017. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142337
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Chen, Lili& Hao, Yaru. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1142337
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Chen, Lili& Hao, Yaru. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142337
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142337
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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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
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