Research Article
A Novel Approach to Visualize Arrhythmia Classification Using 1D CNN
@INPROCEEDINGS{10.1007/978-3-031-48888-7_17, author={Madhumita Mishra and T. L Sharath Kumar and U. M Ashwinkumar}, title={A Novel Approach to Visualize Arrhythmia Classification Using 1D CNN}, proceedings={Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I}, proceedings_a={IC4S}, year={2024}, month={1}, keywords={Electrocardiogram Cardiac Arrhythmia 1-Dimensional CNN Graphics interchange format}, doi={10.1007/978-3-031-48888-7_17} }- Madhumita Mishra
T. L Sharath Kumar
U. M Ashwinkumar
Year: 2024
A Novel Approach to Visualize Arrhythmia Classification Using 1D CNN
IC4S
Springer
DOI: 10.1007/978-3-031-48888-7_17
Abstract
Cardiac-related disorders have been one of the major concerns in recent decades. The electrocardiogram, an extensively utilized medical instrument, records the electrical activity of the heart as a wave. Cardiac arrhythmia is a condition of having an irregular heartbeat. Manually identifying irregularities in an ECG wave is a complicated and challenging task. The current work focuses on computationally identifying the ECG wave fluctuations to determine the abnormality in the heartbeat. We propose to use a 1-Dimensional Convolutional Neural Network (CNN) that analyses a given ECG signal data to identify irregularities in the functioning of the heart and represent the associated risks using graphics interchange format (GIF) files of a 3-dimensional heart. We obtained an accuracy score of 96.72% in classifying given ECG data into five different arrhythmia classes. Automated detection and visual representation of cardiac conditions can help medical associates easily interpret ECG signals and determine arrhythmia early.
