Term of Award
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Mathematical Sciences
Committee Member 1
Committee Member 2
Artificial Neural Network (ANN) models have recently become de facto models for deep learning with a wide range of applications spanning from scientific fields such as computer vision, physics, biology, medicine to social life (suggesting preferred movies, shopping lists, etc.). Due to advancements in computer technology and the increased practice of Artificial Intelligence (AI) in medicine and biological research, ANNs have been extensively applied not only to provide quick information about diseases, but also to make diagnostics accurate and cost-effective. We propose an ANN-based model to analyze a patient's electrocardiogram (ECG) data and produce accurate diagnostics regarding possible heart diseases (arrhythmia, myocardial infarct, etc.). Our model is mainly characterized by its simplicity, as it does not require significant computational power to produce the results. We create and test our model using the MIT-BIH and PTB diagnostics datasets, which are real ECG time series datasets from thousands of patients.
Kaur, Mehakpreet, "Artificial Neural Network Models for Pattern Discovery from ECG Time Series" (2020). Electronic Theses and Dissertations. 2129.
Research Data and Supplementary Material
Applied Mathematics Commons, Computer Sciences Commons, Mathematics Commons, Statistics and Probability Commons