Solving the Inverse Problem for Localising the Biomagnetic Activity in the Heart
This thesis develops a comprehensive solution for forward and inverse problems in modeling the human heart, focusing on MCG and ECG datasets. The methodology includes data recording, MRI processing, and constructing a multi-regional model to segment tissues based on characteristics, solving the forward problem. The approach uses Kalman filter and state-space models, followed by the GARCH model, to solve the inverse problem, improving data analysis and source localization accuracy.
This is the first attempt to apply Kalman filtering to MCG data, leveraging experience from brain research (EEG and MEG datasets). The approach has been validated with simulated and real MCG and ECG datasets, showing efficacy in heart activity analysis and potential clinical applications.
The research’s significance lies in its implications for diagnosing and treating heart conditions. The methodology can precisely localize heart activity sources, aiding in diagnosis and intervention planning, such as ablation or pacemaker implantation. The non-invasive localization method using MCG and ECG datasets offers new avenues for heart condition diagnosis and treatment compared to invasive catheter methods.
Simpler inverse problem methods can find source activity in MCG SQUID and ECG electrode datasets without high computational power. This thesis also aims to analyze data from sensors with lower signal-to-noise ratios, like the magnetoelectric sensor being developed in Kiel, which is cost-effective.
This interdisciplinary research presents a novel methodology for analyzing MCG and ECG datasets, potentially revolutionizing heart condition diagnosis. It highlights the significant contributions interdisciplinary research with engineering can make to advancing medical science.
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