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Robust arrhythmia classifier using wavelet transform and support vector machine classification

The Electrocardiogram (ECG) is the most widely used signal in clinical practice for the assessment of cardiac condition. This paper presents a robust arrhythmia classifier based on the combination of wavelet transform and timing features, as well as support vector machine classification technique. T...

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Main Authors: Chia, Nyoke Goon, Hau, Yuan Wen, Jamaludin, Mohd. Najeb
Format: Conference or Workshop Item
Published: 2017
Subjects:
Q Science (General)
Online Access:http://eprints.utm.my/id/eprint/97277/
http://dx.doi.org/10.1109/CSPA.2017.8064959
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spelling my.utm.972772022-09-26T03:27:58Z http://eprints.utm.my/id/eprint/97277/ Robust arrhythmia classifier using wavelet transform and support vector machine classification Chia, Nyoke Goon Hau, Yuan Wen Jamaludin, Mohd. Najeb Q Science (General) The Electrocardiogram (ECG) is the most widely used signal in clinical practice for the assessment of cardiac condition. This paper presents a robust arrhythmia classifier based on the combination of wavelet transform and timing features, as well as support vector machine classification technique. The proposed technique is able to detect a total of 11 different types of arrhythmia. Results show that the average classification accuracy is up to 87.93% using the 46 MIT-BIH offline ECG database as the testing dataset. A user-friendly Graphical User Interface (GUI) is developed to ease the layman users. This proposed tool aims to reduce the workload of cardiac vascular technologist, medical staff and physicians as assisting cardiac monitoring equipment. 2017 Conference or Workshop Item PeerReviewed Chia, Nyoke Goon and Hau, Yuan Wen and Jamaludin, Mohd. Najeb (2017) Robust arrhythmia classifier using wavelet transform and support vector machine classification. In: 13th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2017, 10 - 12 March 2017, Penang, Malaysia. http://dx.doi.org/10.1109/CSPA.2017.8064959
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic Q Science (General)
spellingShingle Q Science (General)
Chia, Nyoke Goon
Hau, Yuan Wen
Jamaludin, Mohd. Najeb
Robust arrhythmia classifier using wavelet transform and support vector machine classification
description The Electrocardiogram (ECG) is the most widely used signal in clinical practice for the assessment of cardiac condition. This paper presents a robust arrhythmia classifier based on the combination of wavelet transform and timing features, as well as support vector machine classification technique. The proposed technique is able to detect a total of 11 different types of arrhythmia. Results show that the average classification accuracy is up to 87.93% using the 46 MIT-BIH offline ECG database as the testing dataset. A user-friendly Graphical User Interface (GUI) is developed to ease the layman users. This proposed tool aims to reduce the workload of cardiac vascular technologist, medical staff and physicians as assisting cardiac monitoring equipment.
format Conference or Workshop Item
author Chia, Nyoke Goon
Hau, Yuan Wen
Jamaludin, Mohd. Najeb
author_facet Chia, Nyoke Goon
Hau, Yuan Wen
Jamaludin, Mohd. Najeb
author_sort Chia, Nyoke Goon
title Robust arrhythmia classifier using wavelet transform and support vector machine classification
title_short Robust arrhythmia classifier using wavelet transform and support vector machine classification
title_full Robust arrhythmia classifier using wavelet transform and support vector machine classification
title_fullStr Robust arrhythmia classifier using wavelet transform and support vector machine classification
title_full_unstemmed Robust arrhythmia classifier using wavelet transform and support vector machine classification
title_sort robust arrhythmia classifier using wavelet transform and support vector machine classification
publishDate 2017
url http://eprints.utm.my/id/eprint/97277/
http://dx.doi.org/10.1109/CSPA.2017.8064959
_version_ 1745562361034440704
score 13.211869

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