Predicting Acute Myocardial Ischemia using Machine Learning applied to Standard 10-second 12-lead ECG

Besomi, Lucas (2019) Predicting Acute Myocardial Ischemia using Machine Learning applied to Standard 10-second 12-lead ECG. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

Prompt identification of acute coronary syndrome (ACS) is a pivotal challenge to reduce the cost burden related to the prolonged hospitalization of patients with minor cardiovascular issues. Non-ST-segment elevation myocardial infarction and unstable angina are complex to identify and delay the diagnosis of ACS, requiring invasive and costly tests. A screening tool exploiting the different temporal-spatial features of the standard 10-second 12-lead electrocardiogram (ECG) and ruling out patients without risk would then be useful in the admission process of such patients. In this study we compared the performance of the following machine learning algorithms in detecting ACS: Gradient Boosting Machine (GBM), Logistic Regression (LogReg) and Artificial Neural Network (ANN). The first two cohorts of the ongoing EMPIRE (ECG Methods for the Prompt Identification of Coronary Events) study were used as a data set, with 750 patients for training and 500 patients for testing. Three different versions of this data set were used: the initial version with all the features, a reduced version with fewer features, and a final version with re-labeled features. The models were evaluated a first time using only ECG-based features, and then with additional clinical features in order to observe any improvement. The best results were obtained with LogReg on the reduced and labeled version of the data set, with a negative predictive value of 93.4%, a specificity of 76.6% and a sensitivity of 75.6% on the test set. No significant changes were noted after adding clinical features to our classifiers. To conclude, reduction and labeling of the data set proved to be beneficial to our model and this latter could be used as a supportive tool when performing the standard 10-second 12-lead ECG at the very early stage of the continuum of care.

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Details

Item Type:	University of Pittsburgh ETD
Status:	Unpublished
Creators/Authors:	Creators Email Pitt Username ORCID Besomi, Lucas leb138@pitt.edu LEB138 0000-0001-9995-133X
ETD Committee:	Title Member Email Address Pitt Username ORCID Committee Chair Sejdic, Ervin esejdic@pitt.edu esejdic 0000-0003-4987-8298 Committee Member Akcakaya, Murat akcakaya@pitt.edu akcakaya 0000-0001-5094-1931 Committee Member El-Jaroudi, Amro amro@pitt.edu amro Committee Member Al-Zaiti, Salah SSA33@pitt.edu SSA33 0000-0002-6862-0658
Date:	10 September 2019
Date Type:	Publication
Defense Date:	12 July 2019
Approval Date:	10 September 2019
Submission Date:	12 July 2019
Access Restriction:	5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Number of Pages:	53
Institution:	University of Pittsburgh
Schools and Programs:	Swanson School of Engineering > Electrical and Computer Engineering
Degree:	MS - Master of Science
Thesis Type:	Master's Thesis
Refereed:	Yes
Uncontrolled Keywords:	cute myocardial ischemia, prompt identification of ACS, EMPIRE, ECG, machine learning
Date Deposited:	10 Sep 2019 14:00
Last Modified:	10 Sep 2019 14:00
URI:	http://d-scholarship.pitt.edu/id/eprint/37112

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• Predicting Acute Myocardial Ischemia using Machine Learning applied to Standard 10-second 12-lead ECG. (deposited 10 Sep 2019 14:00) [Currently Displayed]

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