Supervisor
Taufique Ahmed
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
Electrocardiography (ECG) is a widely used non-invasive method for monitoring cardiac activity and detecting heart abnormalities. However, manual interpretation can be time-consuming and prone to human error, particularly for non-specialists. This study investigates the use of deep learning techniques, specifically convolutional neural networks (CNNs), to automate ECG signal classification using a large-scale dataset. A classical machine learning model is also included as a baseline for comparison. The aim is to evaluate the potential of deep learning as a clinical decision-support tool that can assist healthcare professionals in improving diagnostic accuracy and supporting early detection of cardiac conditions.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Villegas Baldiviezo, Y.
(2025) Automated classification of cardiac anomalies through the analysis of electrocardiogram signals using convolutional neural networks. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.83