ECG arrhythmias classification by combined feature extraction method and neural network
Other Title(s)
عدم تصنيف الانتظام بإشارة قلب الإنسان باستخدام أسلوب مركب و الشبكة العصيونية
Author
Source
Engineering and Technology Journal
Issue
Vol. 32, Issue 3A (31 Mar. 2014), pp.587-596, 10 p.
Publisher
Publication Date
2014-03-31
Country of Publication
Iraq
No. of Pages
10
Main Subjects
Engineering & Technology Sciences (Multidisciplinary)
Medicine
Topics
Abstract AR
أن هذا البحث يقدم مسيطر عصبي (PID) لأخطي لنموذج مركبة ثنائية الأبعاد لكي يحسن استقرارية و أداء الديناميكي للمركبة من خلال تحقيق معدل الدوران المطلوب و تقليل السرعة الجانبية في أقل وقت ممكن لمنع المركبة من الانزلاق خارج المنعطف. إن هيكلية المسيطر العصبي (PID) مبني على أساس الشبكة العصبية و تنغيم عناصر المسيطر (PID) تتم من خلال تقنية خوارزمية أمثلية حشد الجسيمات لأنها خوارزمية سهلة و سريعة التعلم. إن نظام الكبح الفرقي و زاوية توجيه العجلات الأمامية هي اخرج المسيطر العصبي (PID) اللاخطي الذي يسيطر بصورة تلقائية على الحركة الجانبية للمركبة عندما تدور حول المنعطف.
Abstract EN
Electrocardiogram (ECG) became one of the most crucial tool for heart status diagnosis.
Generally, several arrhythmias may appear based on different heart rate or ECG signal morphology variation.
In this paper, a novel combined feature extraction method to present ECG arrhythmias is proposed.
The combination between Wavelet Packet Transform (WPT) entropies and Power Spectrum Density (PSD) is suggested.
For classification, Feed Forward Back propagation Neural Network (FFBPN) is utilized.
The experimental results showed that the proposed method can be beneficial for ECG signal arrhythmias classification.
MIT-BIH Arrhythmia Database was used for algorithm testing.
The proposed method was compared with three state of art methods, where was of better performance reached about 80 %.
The proposed method as well as other methods was tested in noisy environment for comparison investigations.
The suggested method is promising approach for arrhythmias classification.
However, enormous testing data set might significantly improve the results
American Psychological Association (APA)
al-Azzawi, Khaluq Y.. 2014. ECG arrhythmias classification by combined feature extraction method and neural network. Engineering and Technology Journal،Vol. 32, no. 3A, pp.587-596.
https://search.emarefa.net/detail/BIM-371130
Modern Language Association (MLA)
al-Azzawi, Khaluq Y.. ECG arrhythmias classification by combined feature extraction method and neural network. Engineering and Technology Journal Vol. 32, no. 3A (2014), pp.587-596.
https://search.emarefa.net/detail/BIM-371130
American Medical Association (AMA)
al-Azzawi, Khaluq Y.. ECG arrhythmias classification by combined feature extraction method and neural network. Engineering and Technology Journal. 2014. Vol. 32, no. 3A, pp.587-596.
https://search.emarefa.net/detail/BIM-371130
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 595-596
Record ID
BIM-371130