Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye
Tezin Onay Tarihi: 2013
Tezin Dili: İngilizce
Öğrenci: Alperen Güçlü
Danışman: YEŞİM SERİNAĞAOĞLU DOĞRUSÖZ
Özet:Understanding heart’s electrical activity is very important because coronary problems -such as heart attacks, arrhythmia and stroke- are the leading cause of death in the world. Forward and inverse problems of electrocardiography (ECG) are methods that provide detailed information about the electrical activity of the heart. Forward problem of electrocardiography is the estimation of body surface potentials from equivalent cardiac sources. Inverse problem of electrocardiography can be described as estimation of the electrical sources in the heart using the potential measurements obtained from the body surface. Due to spatial smoothing and attenuation that occur within the thorax, inverse ECG problem is ill-posed and the transfer matrix is ill-conditioned. Thus, regularization is needed to find a stable and accurate solution. In this thesis, epicardial potentials used as equivalent cardiac sources to represent electrical activity of the heart and performances of five different regularization methods are compared. These regularization methods are Tikhonov regularization, truncated singular value decomposition, least squares QR factorization, truncated total least squares, and Lanczos truncated total least squares. Results are assessed qualitatively using correlation coefficient (CC) and relative difference measurement star (RDMS) measures. In addition, real and reconstructed surface potential distributions are compared qualitatively. Body surface potential measurements are simulated with different levels of measurement noise. Geometric errors are also included by changing the size and the location of the heart in the mathematical torso model. According to our test results, the performances of the regularization methods in solving the inverse ECG problem depend on the form and amount of the noise.