Title: Early Prediction of Paroxysmal Atrial Fibrillation using Wavelet Transform Methods

Abstract: Paroxysmal Atrial fibrillation is one of the most common complaints of heart disorders that occur as a result of random vibrations of the atria. PAF episode show a serious increase with age, and the next steps are more difficult especially for the elderly. So, diagnosing in the early stages of this disorder is very important for the PAF patients to stop the progression of the disease and to improve the quality of life. For his reason, in this studyitisaimedtobedetectedwhichin5minutesbeforethePAF episodes. The 30-minute data is divided into 10 parts in 5 minutes with 50% overlap. For each part, wavelet transform methods and wavelet entropy are calculated over heart rate variability data. Using these measurements, it is determined whether there is a statistically significant difference between the parts and the early detection performance of PAF was obtained using the k-nearest neighbors classifier. As a result, PAF episode can be statistically distinguished before it occurs and it is determined that the k-nn classifier has about 72% performance 12.5 minutes earlier than a PAF episode.

Keywords: Paroxysmal atrial fibrillation; wavelet transform; k-nearest neighbors

Başlık: Paroksismal Atriyal Fibrilasyon Atağının Dalgacık Dönüşüm Yöntemleriyle Erken Tahmini

Özet: Paroksismal Atriyal fibrilasyon, kulakçıkların gelişi güzel titreşimi sonucunda meydana gelen kalp rahatsızlıkları içerisinde en çok karşılaşılan bir kalp problemidir. Yaşa bağlı olarak ciddi artış gösteren ve sonraki aşamaları özellikle yaşlılar için oldukça zorlayıcıdır. Bu nedenle, hastalığın ilerlemesini durdurmak ve yaşam kalitesini iyileştirmek için bu hastalığın erken tahmin edilmesi çok önemlidir. Bu sebeple, çalışmada PAF atağı geçirmeden önce kaçıncı 5 dakika öncesinde uyarılabileceğinin tespit edilmesi hedeflenmiştir. 30 dakikalık veriler 50% örtüşmeye sahip olacak şekilde 5 dakikalık 10 parçaya ayrılmıştır. Her bir parça için kalp hızı değişkenliği verileri üzerinden dalgacık dönüşüm yöntemleri hesaplanmıştır. Bu ölçümler ile parçalar arasında istatistiksel anlamlı fark olup olmadığı ve k en yakın komşu sınıflandırıcısının PAF atağını tespit performansı tespit edilmiştir. Sonuç olarak istatistiksel olarak PAF atağının geçekleşmeden önce ayrılabildiği ve k-nn sınıflandırıcısının 12.5 dakika öncesine kadar yaklaşık %72 başarıma sahip olduğu tespit edilmiştir.

Anahtar kelimeler: Paroksismal atriyal fibrilasyon; dalgacık dönüşümü; k-yakın komşu

• January CT, Wann LS, Alpert JS, Calkins H, Cigarroa JE, Cleveland JC, Conti JB, Ellinor PT, Ezekowitz MD, Field ME, Murray KT, Sacco RL, Stevenson WG, Tchou PJ, Tracy CM, Yancy CW. 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: A report of the American College of Cardiology/American Heart Association task force on practice guidelines and the heart rhythm society. Journal of The American College of Cardiology 2014; 64(21): 1-76.
• Camm AJ, Kirchhof P, Lip GYH, Schotten U, SAvelieva I, Ernst S et al. Guidelines for the management of atrial fibrillation. European Heart Journal 2010, 31: 2369–2429.
• Uyarel H, Onat A, Yuksel H, Can G, Ordu S, Dursunoglu D. Incidence, prevalence, and mortality estimates for chronic atrial fibrillation in Turkish adults. Archives of the Turkish Society of Cardiology 2008; 36(4): 214-222.
• Zong W, Mukkamala R, Mark RG. A methodology for predicting paroxysmal atrial fibrillation based on ECG arrhythmia feature analysis. Computers in Cardiology 2001; 28: 125–128.
• Schrier G, Kastner P, Marko W. An automatic ECG processing algorithm to identify patients prone to paroxysmal atrial fibrillation. Computers in Cardiology 2001; 28: 133–135.
• Alcaraz R, Arturo M, Jose JR. Role of the P-wave high frequency energy and duration as noninvasive cardiovascular predictors of paroxysmal atrial fibrillation. Computer Methods and Programs in Biomedicine 2015; 119(2): 110-119.
• Langley P, di Bernardo D, Allen J, Bowers E, Smith FE, Vecchietti S, Murray A. Can paroxysmal atrial fibrillation be predicted? Computers in Cardiology 2001; 28: 121-124.
• Maier C, Bauch M, Dickhaus H. Screening and prediction of paroxysmal atrial fibrillation by analysis of heart rate variability parameters. Computers in Cardiology 2001; 28: 129-132.
• Chazal P, Heneghan C. Automated assessment of atrial fibrillation. Computers in Cardiology 2001; 28: 117-120.
• Krstacic G, Gamberger D, Smuc T, Krstacic A. Some important RR interval based paroxysmal atrial fibrillation predictors. Computers in Cardiology 2001; 28: 409-412.
• Park J, Sangwook L, Moongu J. Atrial fibrillation detection by heart rate variability in Poincare plot. BioMedical Engineering OnLine 2009; 38: 1-12.
• Mohebbi M, Ghassemian H. Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal. Computer Methods and Programs in Biomedicine 2012; 105: 40-49.
• Yang ACC, Yin HW. Prediction of paroxysmal atrial fibrillation by footprint analysis. Computers in Cardiology 2001; 28: 401-404.
• PhysioNet, PAF Prediction Challenge Database, 2001. Retrieved from http://www.physionet.org/physiobank/database/afpdb/
• Isler Y, Kuntalp M. Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure. Computers in Biology and Medicine 2007; 37(10): 1502-1510.
• Narin A, Isler Y, Ozer M. Investigating the performance improvement of HRV Indices in CHF using feature selection methods based on backward elimination and statistical significance. Computers in Biology and Medicine 2014; 45: 72-79.
• Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. European Heart Journal 1996; 17: 354-381.
• Addison PS. Wavelet transforms and the ECG: A review. Physiological Measurement 2005; 26(5): R155.
• Orphanidou C, Drobnjak I. Quality assessment of ambulatory ECG using wavelet entropy of the HRV signal. IEEE Journal of Biomedical and Health Informatics 2016; 21(5): 1216-1223.
• Isler Y, Narin A, Ozer M. Comparison of the effects of crossvalidation methods on determining performances of classifiers used in diagnosing congestive heart failure. Measurement Science Review 2015; 15(4): 196-201.
• Duda RO, Hart PW, Stork DG. Pattern Classification. John Wiley and Sons, 2nd Edition, New York, USA, 2001.
• Boon KH, Khalil-Hani M, Malarvili MB, Sia CW. Paroxysmal atrial fibrillation prediction method with shorter HRV sequences. Computer Methods and Programs in Biomedicine 2016; 134: 187-196.
• Narin A, Isler Y, Ozer M. Early prediction of paroxysmal atrial fibrillation using frequency domain measures of heart rate variability. National Congress on Medical Technologies (TIPTEKNO2016), October 27-29, 2016, Antalya, Turkey, pp. 26-29.