مصنِّف جديد لكشف مرض عدم انتظام ضربات القلب مبني على الاستدلال العائم بالشبكات العصبونية
Abstract
تم في هذا البحث التوصُّل إلى بناء مصنِّف جديد لكشف مرض عدم انتظام القلب Cardiac Arrhythmias بالاعتماد على اقتباس إشارة ضربات القلب ECG، حيث يمكن للمصنف كشف مواقع ضربات القلب ضمن الإشارة ECG واستخلاص السمات الخاصة بها وباستخدام هذه السمات يتم اتخاذ القرار بنوع ضربة القلب التي تم كشفها فيما لو كانت معتلة (مريضة) أم طبيعية.
ركَّز البحث على كشف المرضين 1- خوارج الانقباض البطيني PVC و 2- خوارج الانقباض الأذيني PAC. استطاع المصنِّف الجديد كشف المرضين بسبة عالية حيث بلغ وسطي دقة الكشف 97.56%.
تم تطوير المصنِّف جديد بناءً على خوارزميات الاستدلال العائم باستخدام الشبكات العصبونية ANFIS، حيث يحتوي على شبكتين عصبونيتين متعاقبتين، تقوم الشبكة الأولى بفرز ضربات القلب الطبيعية من المعتلة في حين تقوم الشبكة الثانية بكشف نوع المرض في الضربات المعتلة فقط. لقد أمَّنت هذه البنية الجديدة فعالية ودقة كشف أعلى مقارنةً بالمُصَنِّفات المعروفة عالمياً.
It is found in this research to adopt a new classifier for diagnosing Cardiac Arrhythmias depending on detecting the Electrocardiograph (ECG), where the classifier can identify heart beats and extract its features. Using these features we can decide if the heart beat is healthy or disordered.
Beside detection normal heart beats, the research focused on detection two diseases:
1. Premature Ventricular Contraction PVC.
2. Premature Atrial Contraction PAC.
The new classifier diagnosed the two diseases with a very high quality where the accuracy average is 97.56%.
The new classifier is developed depending on algorithms of ANFIS Adaptive Neural Fuzzy Inference System. System includes two consecutive neural networks; first one sorts the heart beats to two types: normal and abnormal were the second diagnose the disease of the disordered heartbeats only.
This new classifier offered higher levels of efficiency and accuracy in the comparison with the internationally known classifiers.
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