Junctional Ectopic Tachycardia (JET) is a cardiac arrhythmia which occurs immediately after open heart surgery in young children. In specific populations, JET has extremely high incidence, of up to 50%. There has not been a specific mechanism elucidated by clinical data or basic science. As a widely used vertebrate cardiovascular biological model, zebrafish heart is being studied to reveal the leading reasons of JET. Optical mapping (OM) techniques provide an effective approach to observe cardiac functionality by recording zebrafish heart action potential propagation. However, processing of vast amount of OM data also poses challenges on fast and accurate processing, measurement and interpretation. This thesis presents novel automated pipelines for processing zebrafish heart OM data and identifying pacemaking regions from it through signal analysis. We first introduce a preprocessing pipeline for enhancing very low signal-to-noise ratio original OM data, which involves spatial-temporal smoothing, cycle averaging, drifting correction and scaling. After that, we present a computer assisted OM signal manually labeling pipeline, which reduces the manual workload significantly by clustering spatially adjacent similar signals. Furthermore, we make physiologically relevant measurements on OM data and do statistical analysis comparing different labeled regions. Finally, we propose a two-step signal clustering based method to divide atrium into different functional regions followed by pacemaking region identification. We present the formulation of these methods and discuss their validity and performance in various aspects. The work presented in this thesis could lead to significantly faster and larger scaled experimentation in optical mapping related physiology research.
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Thesis advisor: Beg, Mirza Faisal
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