AUTOMATIC DETECTION OF ATRIAL FIBRILLATION BASED ON RR INTERVAL

Abstract

Heart diseases are rapidly increasing worldwide and in our country. This increase causes difficulties in the diagnosis processes of heart diseases. Considering these problems, the studies of engineering applications related to medical science give effective results in terms of solutions. By means of engineering devices and algorithms, positive contributions are made to medical applications. These contributions assist physicians especially in the diagnosis stages and speed up these processes. In this study, a new algorithm is developped so that Atrial Fibrillation (AF), which is the most common type of arrhythmia encountered, can be automatically detected at a high success rate. Electrocardiogram (ECG) data used in this study were obtained from physiobank ATM database. 31 samples of Atrial Fibrillation Rhythm (AFR) and 31 samples of Normal Sinus Rhythm (NSR) were obtained from this database. RR Interval (RRI) sequences being 12 hours long are used in the study. The change of the RRI sequences is an important parameter for AF. The RRI sequences are re-sampled using signal pre-processing techniques. The Discrete Wavelet Transform (DWT) was then applied to the resampled signals. In this way, feature extraction process is performed and the wavelet energies of these signals are visually examined with boxplot. The wavelet energies of the RRI sequences are classified by the Support Vector Machine (SVM). Finally, AFR and NSR are successfully separated as 99.60% achievement.

Keywords

• Atrial fibrillation,Discrete wavelet transform,Support vector machine,Normal sinus rhythm,RR interval

ATRİYAL FİBRİLASYONUN RR ARALIĞI İLE OTOMATİK TESPİTİ

Abstract

Kalp hastalıkları dünya genelinde ve ülkemizde hızlı bir biçimde artmaktadır. Bu artış kalp hastalıklarının tanı süreçlerinde zorlukların oluşmasına neden olmaktadır. Bu sorunlar düşünüldüğünde mühendislik uygulamalarının tıp bilimi ile ilgili olan çalışmaları çözümler açısından etkili sonuçlar vermektedir. Mühendislik sayesinde geliştirilen cihazlar ve algoritmalar sayesinde tıp uygulamalarına olumlu katkılar sağlanmaktadır. Bu uygulamalar özellikle hekimlere tanı aşamalarında yardımcı olmakta ve bu süreçleri hızlandırmaktadır. Bu çalışmada en sık rastlanan aritmi çeşidi olarak karşımıza çıkan Atriyal Fibrilasyon’un (AF) otomatik olarak tespitinin yüksek başarı oranında yapılması tasarlanmıştır. Bu çalışmada kullanılan Elektrokardiyogram (EKG) verileri, Phsiyobank ATM veritabanından elde edilmiştir. Bu veritabınından 31 adet Atriyal Fibrilasyon Ritmi (AFR) ve 31 adet Normal Sinüs Ritmi (NSR) olan sinyaller alınmıştır. Bu sinyaller 12’şer saatlik uzunlukta olup çalışmada RR Aralıkları dizileri kullanılmıştır. RRA dizilerinin değişimi AF için önemli bir parametre olarak karşımıza çıkmaktadır. Sinyal işleme teknikleri ile RR Aralıkları zaman ekseninde yeniden örneklenmiştir. Ardından yeniden örneklenen sinyallere Ayrık Dalgacık Dönüşümü (ADD) uygulanmıştır. Bu sayede özellik çıkarımı işlemi yapılmış ve bu sinyallerin dalgacık enerjileri boxplot ile görsel olarak incelenmiştir. RRA dizilerinin dalgacık enerjileri Destek Vektör Makinası (DVM) ile sınıflandırma işlemine tabi tutulmuş ve %99,60 oranında başarıyla AFR ve NSR birbirinden ayrılmıştır. 

Keywords

• Atriyal fibrilasyon,Ayrık dalgacık dönüşümü,Destek vektör makinası,Normal sinüs ritim,RR aralığı

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