A NEW METHOD FOR THE AUTOMATIC DETECTION OF VENTRICULAR AND ATRIAL PREMATURE CONTRACTIONS

Abstract

ECG signals used in the diagnosis of cardiovascular diseases are very important in terms of continuous recording and evaluation during the monitoring of these diseases, determination of appropriate diagnosis and treatment, and observation of possible complications. The most common disturbances among heart diseases are arising from arrhythmias. In this study, it was aimed to detect the cardiac arrhythmias APC and PVC automatically in the computer environment to provide convenience to the physician. In this context, ECG signals were first taken from the MIT-BIH Arrhythmia database and critical points P, Q, R, S, T on the signals were determined. After then, ANN was used for arrhythmia classification as APC, PVC and NSR. It was determined that the best result among the different ANN constructions was obtained with the MLPNN and the accuracy of the test was determined as 99.78% with 3-fold cross-validation and 99.89% with 10-fold cross-validation.

Keywords

• Electrocardiogram,Atrial Premature Complex,Ventricular Premature Complex,Artificial Neural Networks

KARINCIK VE KULAKÇIK ERKEN VURULARININ OTOMATİK TESPİTİNE DAYALI YENİ BİR YAKLAŞIM

Abstract

Kalp-damar hastalıklarının tanısında kullanılan Elektrokardiyogram (EKG) işaretleri, bu hastalıklarının izlenmesi sürecinde sürekli olarak kaydedilip değerlendirilmeleri, uygun tanı ve tedavinin belirlenmesi ve oluşabilecek komplikasyonların gözlemlenmesi açısından oldukça önem taşımaktadır. Kalp hastalıkları arasında en sık karşılaşılan rahatsızlıklar, aritmilerden kaynaklanmaktadır. Bu çalışmada, kalp aritmilerinden olan Erken Kulakçık Vurusu (APC) ve Erken Karıncık Vurusunu (PVC) bilgisayar ortamında otomatik tespit ederek hekime kolaylık sağlamak hedeflenmiştir. Bu kapsamda, ilk olarak MIT-BIH Aritmi veri tabanından EKG sinyalleri alınmış ve sinyaller üzerinde bulunan P, Q, R, S, T kritik noktaları tespit edilmiştir. Sonrasında, Yapay Sinir Ağları (YSA) kullanılarak APC, PVC ve Normal Sinüs Ritmi (NOR) olarak aritmi sınıflandırılması yapılmıştır. Farklı YSA yapıları arasında en iyi sonucun Çok Katmanlı Algılayıcı (ÇKA) ile elde edildiği görülmüş ve sınıflandırmada test doğruluğunun 3 katlı çapraz doğrulama ile %99.78, 10 katlı çapraz doğrulama ile de %99.89 olduğu belirlenmiştir.  

Keywords

• Elektrokardiyogram,Erken Kulakçık Vurusu,Erken Karıncık Vurusu,Yapay Sinir Ağları

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