Adaptive multivariate solution schemes for inverse electrocardiography problem

---

Tezin Türü: Doktora

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

Tezin Onay Tarihi: 2018

Öğrenci: ÖNDER NAZIM ONAK

Danışman: YEŞİM SERİNAĞAOĞLU DOĞRUSÖZ

Özet:

Electrocardiographic Imaging (ECGI) is an emerging medical imaging modality to visualizetheheart’selectricalactivity. Ithasapromisingpotentialfordiagnosingcardiac abnormalities and facilitate the planning and execution of necessary treatments. Visualizing heart’s electrical activity requires solving the ill-posed inverse electrocardiography (ECG) problem. Despite the considerable efforts and improvements in this field, there exist some limitations and challenges that hinder its application to daily clinical practice. Hence, the inverse ECG problem still attracts the attention of researchers. Since the inverse ECG problem has a ill-posed characteristic, it is necessary to regularizetheproblembyimposingconstraintsbasedonpriorinformationaboutthesolution. Although, several regularization methods have been applied to solve the inverse ECGproblem,noneofthethemhasbeenacceptedasanoptimaltechnique. Because,eachmethodhaslimitationsandthereexistsomecaseswheretheyhaveprosandcons in terms of accuracy, computational complexity and required prior information about the solution. This study focuses on developing adaptive methods that do not claim strong assumptions about the functional form of the unknown epicardial potential distribution and requires less or relatively easily obtainable prior information compared to traditional inverse problem solution techniques. In order to reach these goals the inverse ECG problem is handled both from statistical and deterministic solution techniques perspectives. Firstly, minimum relative entropy method is adopted as an alternative statisticalsolutiontechniqueforinverseECGproblemandeffectsofmethodparameters are comprehensively assessed. From deterministic solution technique perspective, we have proposed multivariate adaptive spline-based method in order to decrease the number of unknown in the problem while increasing the estimation accuracy by taking advantage of local support property of spline-based approaches.