Browsing by Author "Hendryx, Emily"

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    Identifying ECG Clusters in Congenital Heart Disease
    (2015-04-23) Hendryx, Emily; Riviere, Beatrice M.; Rusin, Craig; Cox, Steven; Dabaghian, Yuri
    This thesis presents a method of clustering ECG morphologies for the identification of individual ECG features in congenital heart disease. Clustering is performed on the computed heart dipole moment magnitude using k-medoids clustering with variants of dynamic time warping. The method is applied to both synthetic data and patient data with different parameter values for classic and derivative dynamic time warping. A deterministic k-medoids algorithm demonstrates poor clustering results on both data sets, but an iterative approach with random initialization shows marked improvement. The synthetic data clusters are generally well-defined with the expected number of clusters. Though the patient data derivative results are inconclusive, upon closer examination, the clustering results from classic dynamic time warping with a small warping window seem sensible. Through this project, the groundwork is laid for the future classification of ECG recordings and the development of predictive models in patients with congenital heart disease.
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    Subset Selection and Feature Identification in the Electrocardiogram
    (2018-04) Hendryx, Emily
    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.
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    Subset Selection and Feature Identification in the Electrocardiogram
    (2018-04-19) Hendryx, Emily; Riviere, Beatrice M.; Rusin, Craig G.
    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.