Yang, Ting2020-05-042020-05-042018-01https://hdl.handle.net/11299/213100University of Minnesota Ph.D. dissertation. January 2018. Major: Biomedical Engineering. Advisor: Bin He. 1 computer file (PDF); viii, 77 pages.This dissertation research aims to develop and evaluate methods for noninvasive cardiac imaging of activation sequence and activation recovery interval (ARI), and localization of ventricular arrhythmias. It includes (1) developing a novel imaging method (SSF, Spatial gradient Sparse in Frequency domain) for the reconstruction of activation sequences in ventricular arrhythmias, (2) developing ARI imaging technique to reconstruct the ARI maps in premature ventricular contraction (PVC) patients from body surface potential maps, (3) proposing a CNN-based (convolutional-neural-network-based) method to localize origins of PVCs from 12-lead electrocardiography (ECG). SSF is implemented in the frequency domain, and the activation time was encoded in the phase information of the solution. The performance of SSF was evaluated in computer simulation and a swine model with myocardial infarction. SSF is the first noninvasive imaging method reported that could reconstruct the reentry circuit in 3-dimensional space. SSF achieved better performance with less computational time. ARI imaging reconstructed 3D ARI maps in ventricles, which were compared with the endocardial ARI maps from CARTO recordings. From the analysis of 100 PVC beats in ten patients, the results suggest that it could serve as an alternative of evaluating global dispersion of ventricular repolarization and could guide ablation procedure in PVC patients. The CNN-based method consists of two CNNs (Segment CNN and Epi-Endo CNN). The inputs are from 12-lead ECG. The origins of PVC are localized by calculating the weighted center of gravity of classification returned by the CNNs. It was evaluated in computer simulation and in 90 PVC beats from nine patients. The results demonstrate the capability and merits of the proposed method for localization of PVC. This work suggests a new approach for cardiac source localization of origins of arrhythmias using only the 12-lead ECG by means of CNN.enActivation Recovery IntervalActivation SequenceCardiac Electrical ImagingConvolutional Neural NetworkPremature Ventricular ContractionVentricular ArrhythmiaThesis or Dissertation