Term of Award

Summer 2020

Degree Name

Master of Science in Mathematics (M.S.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


Department of Mathematical Sciences

Committee Chair

Ionut Iacob

Committee Member 1

Goran Lesaja

Committee Member 2

Marcel Ilie


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.

OCLC Number


Research Data and Supplementary Material