TY  - JOUR
T1  - AUTOMATIC DETECTION OF ATRIAL FIBRILLATION BASED ON RR INTERVAL
TT  - ATRİYAL FİBRİLASYONUN RR ARALIĞI İLE OTOMATİK TESPİTİ
AU  - Bilgin, Süleyman
AU  - Güzeler, Anıl Can
PY  - 2019
DA  - September
Y2  - 2019
DO  - 10.21923/jesd.512030
JF  - Mühendislik Bilimleri ve Tasarım Dergisi
JO  - MBTD
PB  - Süleyman Demirel University
WT  - DergiPark
SN  - 1308-6693
SP  - 487
EP  - 497
VL  - 7
IS  - 3
LA  - en
AB  - Heart diseases are rapidly increasing worldwide and in our country. Thisincrease causes difficulties in the diagnosis processes of heart diseases. Consideringthese problems, the studies of engineering applications related to medicalscience give effective results in terms of solutions. By means of engineeringdevices and algorithms, positive contributions are made to medicalapplications. These contributions assist physicians especially in the diagnosisstages and speed up these processes. In this study, a new algorithm isdevelopped so that Atrial Fibrillation (AF), which is the most common type ofarrhythmia encountered, can be automatically detected at a high success rate.Electrocardiogram (ECG) data used in this study were obtained from physiobank ATMdatabase. 31 samples of Atrial Fibrillation Rhythm (AFR) and 31 samples ofNormal 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 RRIsequences is an important parameter for AF. The RRI sequences are re-sampled usingsignal pre-processing techniques. The Discrete Wavelet Transform (DWT) was thenapplied to the resampled signals. In this way, feature extraction process isperformed and the wavelet energies of these signals are visually examined withboxplot. The wavelet energies of the RRI sequences are classified by the SupportVector Machine (SVM). Finally, AFR and NSR are successfully separated as 99.60%achievement.
KW  - Atrial fibrillation
KW  - Discrete wavelet transform
KW  - Support vector machine
KW  - Normal sinus rhythm
KW  - RR interval
N2  - Kalp hastalıkları dünyagenelinde ve ülkemizde hızlı bir biçimde artmaktadır. Bu artış kalphastalıklarının tanı süreçlerinde zorlukların oluşmasına neden olmaktadır. Busorunlar düşünüldüğünde mühendislik uygulamalarının tıp bilimi ile ilgili olançalışmaları çözümler açısından etkili sonuçlar vermektedir. Mühendisliksayesinde geliştirilen cihazlar ve algoritmalar sayesinde tıp uygulamalarınaolumlu katkılar sağlanmaktadır. Bu uygulamalar özellikle hekimlere tanıaşamalarında yardımcı olmakta ve bu süreçleri hızlandırmaktadır. Bu çalışmadaen sık rastlanan aritmi çeşidi olarak karşımıza çıkan Atriyal Fibrilasyon’un(AF) otomatik olarak tespitinin yüksek başarı oranında yapılmasıtasarlanmıştır. Bu çalışmada kullanılan Elektrokardiyogram (EKG) verileri, PhsiyobankATM veritabanından elde edilmiştir. Bu veritabınından 31 adet AtriyalFibrilasyon Ritmi (AFR) ve 31 adet Normal Sinüs Ritmi (NSR) olan sinyalleralınmıştır. Bu sinyaller 12’şer saatlik uzunlukta olup çalışmada RR Aralıklarıdizileri kullanılmıştır. RRA dizilerinin değişimi AF için önemli bir parametreolarak karşımıza çıkmaktadır. Sinyal işleme teknikleri ile RR Aralıkları zamanekseninde yeniden örneklenmiştir. Ardından yeniden örneklenen sinyallere AyrıkDalgacık Dönüşümü (ADD) uygulanmıştır. Bu sayede özellik çıkarımı işlemiyapılmış ve bu sinyallerin dalgacık enerjileri boxplot ile görsel olarakincelenmiştir. RRA dizilerinin dalgacık enerjileri Destek Vektör Makinası (DVM)ile sınıflandırma işlemine tabi tutulmuş ve %99,60 oranında başarıyla AFR veNSR birbirinden ayrılmıştır.
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UR  - https://doi.org/10.21923/jesd.512030
L1  - https://dergipark.org.tr/en/download/article-file/804367
ER  -