Premature Ventricular Contraction Classification Using Wavelet Features And Probabilistic Neural Network

Electrocardiogram (ECG) classification is vital in determining the health condition of an individual. Cardiologist examine ECG as a means of detecting heart condition and dangerous heart condition. Particularly, accurate detection of Premature Ventricular Contraction (PVC) is essential to prepare fo...

Full description

Saved in:

Bibliographic Details
Main Author:	Amir Hamzah, Nur Asyiqin
Format:	Thesis
Published:	2010
Subjects:	RC0321 Neuroscience Biological psychiatry Neuropsychiatry
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-mmu-ep.3473
record_format	uketd_dc
spelling	my-mmu-ep.34732021-09-21T06:56:37Z Premature Ventricular Contraction Classification Using Wavelet Features And Probabilistic Neural Network 2010-09 Amir Hamzah, Nur Asyiqin RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry Electrocardiogram (ECG) classification is vital in determining the health condition of an individual. Cardiologist examine ECG as a means of detecting heart condition and dangerous heart condition. Particularly, accurate detection of Premature Ventricular Contraction (PVC) is essential to prepare for the possible inception of life threatening arrhythmia. 2010-09 Thesis http://shdl.mmu.edu.my/3473/ https://proxyvlib.mmu.edu.my/login?url=http://library.mmu.edu.my/library2/diglib/mmuetd/ masters Multimedia University Faculty of Engineering and Technology
institution	Multimedia University
collection	MMU Institutional Repository
topic	RC0321 Neuroscience Biological psychiatry Neuropsychiatry
spellingShingle	RC0321 Neuroscience Biological psychiatry Neuropsychiatry Amir Hamzah, Nur Asyiqin Premature Ventricular Contraction Classification Using Wavelet Features And Probabilistic Neural Network
description	Electrocardiogram (ECG) classification is vital in determining the health condition of an individual. Cardiologist examine ECG as a means of detecting heart condition and dangerous heart condition. Particularly, accurate detection of Premature Ventricular Contraction (PVC) is essential to prepare for the possible inception of life threatening arrhythmia.
format	Thesis
qualification_level	Master's degree
author	Amir Hamzah, Nur Asyiqin
author_facet	Amir Hamzah, Nur Asyiqin
author_sort	Amir Hamzah, Nur Asyiqin
title	Premature Ventricular Contraction Classification Using Wavelet Features And Probabilistic Neural Network
title_short	Premature Ventricular Contraction Classification Using Wavelet Features And Probabilistic Neural Network
title_full	Premature Ventricular Contraction Classification Using Wavelet Features And Probabilistic Neural Network
title_fullStr	Premature Ventricular Contraction Classification Using Wavelet Features And Probabilistic Neural Network
title_full_unstemmed	Premature Ventricular Contraction Classification Using Wavelet Features And Probabilistic Neural Network
title_sort	premature ventricular contraction classification using wavelet features and probabilistic neural network
granting_institution	Multimedia University
granting_department	Faculty of Engineering and Technology
publishDate	2010
_version_	1776101411567697920

Premature Ventricular Contraction Classification Using Wavelet Features And Probabilistic Neural Network

Similar Items