Classification of normal and malignant ventricular arrhythmia ECG rhythms using machine learning tools
The increasing prevalence of heart disease among individuals is a call for alarm, especially since heart disease remains a leading cause of death worldwide. As such, it is of utmost importance to identify any irregularity in the functioning of the heart, at the earliest. Arrhythmia is one such irreg...
Saved in:
Main Author: | Prabhakaran, Sahithya |
---|---|
Other Authors: | Vidya Sudarshan |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/175037 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Application of statistics and machine learning for risk stratification of heritable cardiac arrhythmias
by: Wasan, P.S., et al.
Published: (2014) -
PHYSICS VS MACHINE LEARNING: TOPOLOGICAL CLASSIFICATION WITH MACHINE LEARNING & ENHANCED MACHINE LEARNING WITH QUANTUM PROPERTIES
by: MA NANNAN
Published: (2023) -
Use of feature vectors and constellation maps for analysis of normal sinus rhythm, supraventricular arrhythmia and sudden cardiac death electrocardiogram (ECG) traces
by: Santos, Marc Ericson C., et al.
Published: (2011) -
Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review
by: Ong, Wilson, et al.
Published: (2023) -
Spectrogram analysis of electrocardiogram with normal sinus rhythm, arrhythmia and atrial fibrillation
by: Segismundo, M. J., et al.
Published: (2012)