AUTOMATIC DETECTION OF ATRIAL FIBRILLATION BASED ON RR INTERVAL

Year 2019, Volume: 7 Issue: 3, 487 - 497, 15.09.2019

Anıl Can Güzeler Süleyman Bilgin

https://doi.org/10.21923/jesd.512030

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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