QT ZAMAN ARALIĞININ GAUSS KARIŞIM MODELİ VE YAPAY SİNİR AĞI TABANLI TESPİTİ

Öz

   Günümüzde yarı-parametrik tanımlanan yapay sinir ağı tabanlı olasılıksal yöntemler biyolojik sinyallerin işlenmesi örüntü tanımasında aktif olarak kullanılmaktadır. Bu çalışmada, EKG sinyallerinin önemli bir zaman aralığı olan QT süresinin belirlenmesi ve sınıflandırılması için yarı-parametrik Gauss karışım modeli tabanlı yapay sinir ağı modeli gerçeklenmiştir. Bu kapsamda, zamana bağlı değişen kalp ritim sinyallerinin, eğitimi ve sınıflandırılması olasılıksal metotların gözetimli ve gözetimsiz eğitimi ile tamamlanmış, ayrıca yeni bir fikir olarak karşılaştırma algoritması statik yapay sinir ağları için sunulmuştur. Önerilen algoritma ile 105 PHYSIONET QT veritabanı verileri ve 4 gerçek denekten alınmış veriler işlenmiştir. Gerekli eğitimler tamamlandıktan sonra, sunulan algoritma %97,11 hassasiyet, %94,27 pozitif belirleyicilik ve %4,2 hata oranı ile QRS kompleksi ve T dalgasını saptayabilmiş, ayrıca 3,1 milisaniye ortalama hata değeri ve 5,62 milisaniye standart sapma değeri ile QT zaman aralığını bulabilmiştir. Sonuçlara göre, önerilen algoritma değişik EKG sinyalleri için yüksek performansta sınıflama ve ayrıştırma işlemini gerçekleştirebilmiştir.

Anahtar Kelimeler

• QT ölçümü,T-dalgası,EKG sinyal sınıflaması,yapay sinir ağları,Gauss karışım modeli

GAUSSIAN MIXTURE MODEL AND NEURAL NETWORK BASED DETERMINATION OF QT DURATION

Öz

   Nowadays, probabilistic methods based on semi-parametric neural networks have been used to signal processing in biological signals with individual characteristics. The main objective of this study was to develop a semi-parametric neural network based on Gaussian mixture model to perform the QT measurement and classification. For this purpose, a comparison algorithm evaluating time-series cardiac signals was established with training by supervised and unsupervised learning, and the comparison algorithm was presented in order to static neural networks. The proposed algorithm has been tested on the data from 4 normal subjects and 105 additional normal data sets from PHYSIONET QT database. After the improvement by the proposed algorithm, we observed that the QT-measurements were done with 3.1 milliseconds of the mean values and 5.62 milliseconds of standard errors, when QRS complexes and T waves are detected at the rate of 97.11% sensitivity, 94.27% positive predictivity and 4.2% error value, respectively. The results suggested that the proposed algorithm achieved a classification and discrimination of various ECG signals at a high performance level.

Anahtar Kelimeler

• QT measurement,T wave,ECG signal classification,artificial neural networks,Gaussian mixture model

Kaynakça

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