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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Early Prediction of Paroxysmal Atrial Fibrillation using Wavelet Transform Methods

Paroksismal Atriyal Fibrilasyon Atağının Dalgacık Dönüşüm Yöntemleriyle Erken Tahmini

How to cite: Narin A, İşler Y, Özer M. Early prediction of paroxysmal atrial fibrillation using wavelet transform methods. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2019; 2(2): 111-114. DOI: 10.54856/jiswa.201912077

Full Text: PDF, in Turkish.

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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


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