Clustering of ECG Signals Based on Fuzzy Neural Network with Initial Weights Generated by Genetic Algorithm

Majlesi Journal of Electrical Engineering

مقاله 1، دوره 8، شماره 1، خرداد 2014، صفحه 1-9   

نوع مقاله: Review Article

نویسندگان

Elaheh Sayari^{* 1}؛ Mahdi Yaghoobi²

¹Computer & Electrical Engineering Department ,Mashhad Branch Islamic Azad University, Mashhad, Iran

²Electrical and Computer Engineering Department, Mashhad Branch, Islamic Azad University, Mashhad, Iran

چکیده

Early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through appropriate treatment. Whereas clustering of electrocardiogram (ECG) signals will help to identification of heart diseases as soon as possible. In this regard, neural network and fuzzy logic have been used in many application areas while each of them has advantages and disadvantages. Thus, the present paper utilizes the proposed fuzzy neural network (FNN) with initial weights generated by genetic algorithm (GFNN) for the sake of improvement training speed, accurate and to reduce the chance of the FNN getting stuck on a local minimum.Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat and atrial fibrillation beat) obtained from the PhysioBank database was clustered by the proposed GFNN model. Model evaluation results indicate that the proposed model can perform more accurately and less training speed than the conventional statistical methods, a single ANN and FNN. The total clustering accuracy of the GFNN model is 98.23%. تجمیع إشارات تخطیط القلب مبنی على ضبابی الشبکات العصبیة مع الأوزان المبدئیة التی تم إنشاؤها بواسطة الخوارزمیة الجینیةالاکتشاف المبکر لأمراض القلب / تشوهات یمکن أن یطیل الحیاة وتحسین نوعیة المعیشة من خلال العلاج المناسب. فی حین تجمیع الکهربائی و(ECG) إشارات تساعد على التعرف على أمراض القلب فی أقرب وقت ممکن. وفی هذا الصدد، تم استخدام شبکات الذکاء والمنطق الضبابی فی العدید من مجالات التطبیق فی حین أن کل واحد منهم له مزایا وعیوب. وهکذا، وتستخدم هذه الورقة الشبکة العصبیة غامض المقترحة (FNN) مع الأوزان المبدئیة التی تم إنشاؤها بواسطة الخوارزمیة الجینیة (GFNN) من أجل سرعة التدریب تحسین ودقیقة وتقلل من فرصة للFNN أن یعلقوا على ما لا یقل المحلی.وقد تتجمع أربعة أنواع من یدق تخطیط القلب (ضربات العادی، قصور القلب الاحتقانی فوز، البطین ضربات اضطراب النظم التسرعی والأذینی ضربات الرجفان) تم الحصول علیها من قاعدة البیانات PhysioBank من النموذج GFNN المقترحة. وتشیر نتائج التقییم النموذجیة أن یکون النموذج المقترح یمکن أن تؤدی بشکل أکثر دقة وأقل سرعة التدریب من الأسالیب الإحصائیة التقلیدیة، وANN واحد وFNN. مجموع دقة تجمیع نموذج GFNN هی 98.23٪ 所得电力由遗传算法初始权模糊神经网络抽象早期发现心脏疾病/异常能延长生命和提高生活通过适当治疗的质量。而心电图集群（ECG）信号将尽快帮助心脏疾病的鉴别。在这方面，神经网络和模糊逻辑已经在许多应用领域中使用，而每个人都有优点和缺点。因此，本文利用所提出的模糊神经网络（FNN）与遗传算法（GFNN）生成的初始权重改进训练速度，准确起见，减少了模糊神经网络的机会陷入一个局部最小。从PhysioBank数据库中获得四种类型的心电图次（正常跳动，充血性心脏衰竭的跳动，室性心律失常节奏和房颤拍）被提出GFNN模型集群。模型的评估结果表明，该模型能更准确地执行，比传统的统计方法，单一的人工神经网络和模糊神经网络训练速度更低。该GFNN模型的总聚类准确率是98.23％。

کلیدواژه‌ها

Clustering؛ en؛ Electrocardiogram؛ Signals؛ Neural network؛ fuzzy logic؛ Beat

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