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dc.contributor.authorHendryx, Emily
dc.date.accessioned 2018-06-26T13:59:55Z
dc.date.available 2018-06-26T13:59:55Z
dc.date.issued 2018-04
dc.identifier.citation Hendryx, Emily. "Subset Selection and Feature Identification in the Electrocardiogram." (2018) https://hdl.handle.net/1911/102271.
dc.identifier.urihttps://hdl.handle.net/1911/102271
dc.description This work was also published as a Rice University thesis/dissertation.
dc.description.abstract Each feature in the electrocardiogram (ECG) corresponds to a different part of the cardiac cycle. Tracking changes in these features over long periods of time can offer insight regarding changes in a patient’s clinical status. However, the automated identification of features in some patient populations, such as the pediatric congenital heart disease population, remains a nontrivial task that has yet to be mastered. Working toward a solution to this problem, this thesis outlines an overall framework for the identification of individual features in the ECGs of different populations. With a goal of applying part of this framework retrospectively to large sets of patient data, we focus primarily on the selection of relevant subsets of ECG beats for subsequent interpretation by clinical experts. We demonstrate the viability of the discrete empirical interpolation method (DEIM) in identifying representative subsets of beat morphologies relevant for future classification models. The success of DEIM applied to data sets from a variety of contexts is compared to results from related approaches in numerical linear algebra, as well as some more common clustering algorithms. We also present a novel extension of DEIM, called E-DEIM, in which additional representative data points can be identified as important without being limited by the rank of the corresponding data matrix. This new algorithm is evaluated on two different data sets to demonstrate its use in multiple settings, even beyond medicine. With DEIM and its related methods identifying beat-class representatives, we then propose an approach to automatically extend physician expertise on the selected beat morphologies to new and unlabeled beats. Using a fuzzy classification scheme with dynamic time warping, we are able to provide preliminary results suggesting further pursuit of this framework in application to patient data.
dc.format.extent 251 pp
dc.title Subset Selection and Feature Identification in the Electrocardiogram
dc.type Technical report
dc.date.note April 2018
dc.identifier.digital TR18-02
dc.type.dcmi Text


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