Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability

Atrial fibrillation (AF) is the most common arrhythmia type and its early stage is paroxysmal atrial fibrillation (PAF). PAF affects negatively the quality of life by causing dyspnea, chest pain, feeling of excessive fatigue, and dizziness. In this study, our aim is to predict the onset of paroxysmal atrial fibrillation (PAF) events so that patients can take precautions to prevent PAF events. We use an open data from Physionet, Atrial Fibrillation Prediction Database. We construct our approach based on the heart rate variability (HRV) analysis. Short-term HRV analysis requires 5-minute data so that each dataset was divided into 5-minute data segments. HRV features for each segment are calculated from time-domain measures and frequency-domain measures using power spectral density estimations of fast Fourier transform, Lomb–Scargle, and wavelet transform methods. Different combinations of these HRV features are selected by Genetic Algorithm and then applied to k-nearest neighbors classification algorithm. We compute the classifier performances by the 10-fold cross-validation method. The proposed approach results in 92% sensitivity, 88% specificity and 90% accuracy in the 2.5–7.5 min time interval priors to PAF event. The proposed method results in better classification performance than the similar studies in literature. Comparing the existing studies, we propose that our approach provide better tool to predict PAF events. © 2018 Elsevier B.V.

Eser Adı
[dc.title]
Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability
Yazar
[dc.contributor.author]
Narin, Ali
Yazar
[dc.contributor.author]
İşler, Yalçın
Yazar
[dc.contributor.author]
Özer, Mahmut
Yazar
[dc.contributor.author]
Perc, Matjaž
Yayın Yılı
[dc.date.issued]
2018
Yayıncı
[dc.publisher]
Elsevier B.V.
Yayın Türü
[dc.type]
article
Özet
[dc.description.abstract]
Atrial fibrillation (AF) is the most common arrhythmia type and its early stage is paroxysmal atrial fibrillation (PAF). PAF affects negatively the quality of life by causing dyspnea, chest pain, feeling of excessive fatigue, and dizziness. In this study, our aim is to predict the onset of paroxysmal atrial fibrillation (PAF) events so that patients can take precautions to prevent PAF events. We use an open data from Physionet, Atrial Fibrillation Prediction Database. We construct our approach based on the heart rate variability (HRV) analysis. Short-term HRV analysis requires 5-minute data so that each dataset was divided into 5-minute data segments. HRV features for each segment are calculated from time-domain measures and frequency-domain measures using power spectral density estimations of fast Fourier transform, Lomb–Scargle, and wavelet transform methods. Different combinations of these HRV features are selected by Genetic Algorithm and then applied to k-nearest neighbors classification algorithm. We compute the classifier performances by the 10-fold cross-validation method. The proposed approach results in 92% sensitivity, 88% specificity and 90% accuracy in the 2.5–7.5 min time interval priors to PAF event. The proposed method results in better classification performance than the similar studies in literature. Comparing the existing studies, we propose that our approach provide better tool to predict PAF events. © 2018 Elsevier B.V.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Dili
[dc.language.iso]
eng
Konu Başlıkları
[dc.subject]
Early prediction
Konu Başlıkları
[dc.subject]
Genetic algorithm
Konu Başlıkları
[dc.subject]
Heart rate variability
Konu Başlıkları
[dc.subject]
Paroxysmal atrial fibrillation
Konu Başlıkları
[dc.subject]
Wavelet packet transform
Künye
[dc.identifier.citation]
Narin, A., Isler, Y., Ozer, M. ve Perc, M. (2018). Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability. Physica A: Statistical Mechanics and its Applications, 509, 56–65. doi:https://doi.org/10.1016/j.physa.2018.06.022
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
ISSN
[dc.identifier.issn]
0378-4371
İlk Sayfa Sayısı
[dc.identifier.startpage]
56
Son Sayfa Sayısı
[dc.identifier.endpage]
65
Dergi Adı
[dc.relation.journal]
Physica A: Statistical Mechanics and its Applications
Dergi Cilt Bilgisi
[dc.identifier.volume]
509
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1016/j.physa.2018.06.022
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/5238
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
50
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
28 Şubat 2024 23:52
Google Kontrol
Tıklayınız
events fibrillation approach analysis Atrial predict classification results proposed 5-minute better method features transform measures paroxysmal atrial studies neighbors Different Algorithm Lomb–Scargle wavelet applied methods 10-fold performances Genetic classifier compute algorithm k-nearest selected combinations cross-validation
6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve çerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.

creativecommons
Bu site altında yer alan tüm kaynaklar Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.
Platforms