Combination of conventional regularization methods and genetic algorithms for solving the inverse problem of electrocardiography

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2010

Öğrenci: SEDAT SARIKAYA

Danışman: YEŞİM SERİNAĞAOĞLU DOĞRUSÖZ

Özet:

Distribution of electrical potentials over the surface of the heart, i.e., the epicardial potentials, is a valuable tool to understand whether there is a defect in the heart. However, it is not easy to detect these potentials non-invasively. Instead, body surface potentials, which occur as a result of the electrical activity of the heart, are measured to diagnose heart defects. However the source electrical signals loose some critical details because of the attenuation and smoothing they encounter due to body tissues such as lungs, fat, etc. Direct measurement of these epicardial potentials requires invasive procedures. Alternatively, one can reconstruct the epicardial potentials non-invasively from the body surface potentials; this method is called the inverse problem of electrocardiography (ECG). The goal of this study is to solve the inverse problem of ECG using several well-known regularization methods and using their combinations with genetic algorihm (GA) and finally compare the performances of these methods. The results show that GA can be combined with the conventional regularization methods and their combination improves the regularization of ill-posed inverse ECG problem. In several studies, the results show that their combination provide a good scheme for solving the ECG inverse problem and the performance of regularization methods can be improved further. We also suggest that GA can be initiated succesfully with a training set of epicardial potentials, and with the optimum, over- and under-regularized Tikhonov regularization solutions.