A Convolutional Neural Network Based Deep Network Model for Atrial Fibrillation Detection

Yıl 2021, Cilt: 9 Sayı: 6 - ICAIAME 2021, 230 - 236, 31.12.2021

Fatma Murat Ferhat Sadak Muhammed Talo Özal Yıldırım Yakup Demir

https://doi.org/10.29130/dubited.1011246

Öz

Atrial Fibrillation (AFib) is a common heart rhythm disorder that can occur in the elderly and even young people without any heart disease. AFib can result in a stroke, heart failure, or sudden death.. All of these existing and future concerns require significant efforts in the early diagnosis of AFib around the world.. Electrocardiographic (ECG) waveforms are considered the most reliable method for detecting abnormal heart rhythms such as AFib. However, due to the complexity and non-linearity of ECG signals, it is difficult to analyze these signals manually. Furthermore, the interpretation of ECG data is subjective and may differ amongst specialists.. Therefore, the use of computer-assisted diagnostic (CAD) systems is important for automatic and reliable detection of AFib. CAD systems have the potential to make objective and accurate evaluation of ECG signals. In this paper, automatic AFib detection from ECG signals was performed using deep learning system. A deep network model was proposed within the scope of this study for the use of convolutional neural network (CNN) architecture, one of the deep learning algorithms, in the AFib classification problem. The dataset used includes normal sinus rhythms (SR) as well as AFib and Atrial Flutter (AFL) arrhythmias. By combining AFib and AFL classes, automatic classification of SR and AFib is provided at the model output. The suggested model was tested on a data set of 5000 samples of ECG signals from patients with 2222 SR and 2218 AFib, respectively. The CNN model developed within the scope of this study achieved 95.09% sensitivity, 97.27% specificity and 97.26% precision values, respectively, during the test phase. The accuracy percentageof the model on the test data was 96.17%.

Anahtar Kelimeler

Atrial fibrillation, Convolutional neural networks, Deep learning

Atriyal Fibrilasyon Tespiti için Evrişimli Sinir Ağı Tabanlı Bir Derin Ağ Modeli

Öz

Atriyal Fibrilasyon (AFib), yaşlılarda ve hatta herhangi bir kalp hastalığı olmayan gençlerde bile görülebilen yaygın bir kalp ritim bozukluğudur. AFib; inme, kalp yetmezliği ve ani ölümlere neden olabilir. Tüm bu mevcut ve gelecekteki endişeler, dünya çapında AFib'in erken tespitinde önemli önlemlerin alınmasını gerektirir. Elektrokardiyografik (EKG) dalga formları, AFib gibi anormal kalp ritimlerini saptamak için en güvenilir yöntem olarak kabul edilmektedir. Ancak EKG sinyallerinin karmaşıklığı ve doğrusal olmaması nedeniyle bu sinyalleri manuel olarak analiz etmek zordur. Bunun yanı sıra, EKG sinyallerinin yorumlanması kişiye özgü ve uzmanlar arasında farklılık gösterebilmektedir. Bu nedenle otomatik ve güvenilir bir AFib algılama için bilgisayar destekli teşhis (BDT) sistemlerinin kullanımı önemlidir. BDT sistemleri, EKG sinyallerinin değerlendirilmesinin objektif ve doğru olmasını sağlayacak potansiyele sahiptir. Bu çalışmada, derin öğrenme yapısı kullanılarak EKG sinyallerinden otomatik AFib tespiti gerçekleştirilmiştir. Derin öğrenme algoritmalarından evrişimli sinir ağı (ESA) mimarisinin AFib sınıflandırma probleminde kullanımı için çalışma kapsamında derin bir ağ modeli tasarlanmıştır. Kullanılan verisetinde normal sinüs ritimlerinin (SR) yanısıra AFib ve Atriyal Flutter (AFL) aritmileri bulunmaktadır. AFib ve AFL sınıfları birleştirilerek model çıkışında SR ve AFib ayırımının otomatik yapılması sağlanmıştır. Önerilen model, 2222 SR ve 2218 AFib tanısı alan kişilere ait her biri 5000 örneğe sahip EKG sinyali içeren veri seti üzerinde uygulanmıştır. Çalışma kapsamında hazırlanan ESA modeli, test aşamasında sırasıyla %95.09 hassasiyet, %97.27 özgüllük ve %97.26 kesinlik değerlerine ulaşmıştır. Modelin test verileri üzerindeki doğruluk oranı %96.17 olarak elde edilmiştir.

Anahtar Kelimeler

Atriyal fibrilasyon, Evrişimsel sinir ağları, Derin öğrenme

Kaynakça

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