Yıl 2020, Cilt 8 , Sayı 1, Sayfalar 165 - 174 2020-03-20

KARINCIK VE KULAKÇIK ERKEN VURULARININ OTOMATİK TESPİTİNE DAYALI YENİ BİR YAKLAŞIM
A NEW METHOD FOR THE AUTOMATIC DETECTION OF VENTRICULAR AND ATRIAL PREMATURE CONTRACTIONS

Zahide Elif AKIN [1] , Süleyman BİLGİN [2]


Kalp-damar hastalıklarının tanısında kullanılan Elektrokardiyogram (EKG) işaretleri, bu hastalıklarının izlenmesi sürecinde sürekli olarak kaydedilip değerlendirilmeleri, uygun tanı ve tedavinin belirlenmesi ve oluşabilecek komplikasyonların gözlemlenmesi açısından oldukça önem taşımaktadır. Kalp hastalıkları arasında en sık karşılaşılan rahatsızlıklar, aritmilerden kaynaklanmaktadır. Bu çalışmada, kalp aritmilerinden olan Erken Kulakçık Vurusu (APC) ve Erken Karıncık Vurusunu (PVC) bilgisayar ortamında otomatik tespit ederek hekime kolaylık sağlamak hedeflenmiştir. Bu kapsamda, ilk olarak MIT-BIH Aritmi veri tabanından EKG sinyalleri alınmış ve sinyaller üzerinde bulunan P, Q, R, S, T kritik noktaları tespit edilmiştir. Sonrasında, Yapay Sinir Ağları (YSA) kullanılarak APC, PVC ve Normal Sinüs Ritmi (NOR) olarak aritmi sınıflandırılması yapılmıştır. Farklı YSA yapıları arasında en iyi sonucun Çok Katmanlı Algılayıcı (ÇKA) ile elde edildiği görülmüş ve sınıflandırmada test doğruluğunun 3 katlı çapraz doğrulama ile %99.78, 10 katlı çapraz doğrulama ile de %99.89 olduğu belirlenmiştir.  

ECG signals used in the diagnosis of cardiovascular diseases are very important in terms of continuous recording and evaluation during the monitoring of these diseases, determination of appropriate diagnosis and treatment, and observation of possible complications. The most common disturbances among heart diseases are arising from arrhythmias. In this study, it was aimed to detect the cardiac arrhythmias APC and PVC automatically in the computer environment to provide convenience to the physician. In this context, ECG signals were first taken from the MIT-BIH Arrhythmia database and critical points P, Q, R, S, T on the signals were determined. After then, ANN was used for arrhythmia classification as APC, PVC and NSR. It was determined that the best result among the different ANN constructions was obtained with the MLPNN and the accuracy of the test was determined as 99.78% with 3-fold cross-validation and 99.89% with 10-fold cross-validation.

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Birincil Dil en
Konular Mühendislik, Elektrik ve Elektronik
Yayımlanma Tarihi 2020 Mart 8(1)
Bölüm Araştırma Makalesi \ Research Makaleler
Yazarlar

Orcid: 0000-0001-5358-225X
Yazar: Zahide Elif AKIN
Ülke: Turkey


Orcid: 0000-0003-0496-8943
Yazar: Süleyman BİLGİN (Sorumlu Yazar)
Kurum: AKDENIZ UNIVERSITY
Ülke: Turkey


Tarihler

Başvuru Tarihi : 21 Nisan 2019
Kabul Tarihi : 21 Ağustos 2019
Yayımlanma Tarihi : 20 Mart 2020

APA AKIN, Z , BİLGİN, S . (2020). A NEW METHOD FOR THE AUTOMATIC DETECTION OF VENTRICULAR AND ATRIAL PREMATURE CONTRACTIONS. Mühendislik Bilimleri ve Tasarım Dergisi , 8 (1) , 165-174 . DOI: 10.21923/jesd.556486