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Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features

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Main Authors: Murukesan, L., Murugappan, Muthusamy, Dr., Muhammad Nadeem, Iqbal, Krishinan, Saravanan, Dr.
Other Authors: murukesan.loganathan23@gmail.com
Format: Article
Language:English
Published: American Scientific Publishers 2015
Subjects:
Analysis of Variance
Heart Rate Variability
Probabilistic Neural Network
Sequential Feature Selection
Sudden Cardiac Arrest
Support Vector Machine
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/39430
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spelling my.unimap-394302015-04-13T02:32:00Z Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features Murukesan, L. Murugappan, Muthusamy, Dr. Muhammad Nadeem, Iqbal Krishinan, Saravanan, Dr. murukesan.loganathan23@gmail.com murugappan@unimap.edu.my mr.nadeemiqbal@gmail.com Analysis of Variance Heart Rate Variability Probabilistic Neural Network Sequential Feature Selection Sudden Cardiac Arrest Support Vector Machine Link to publisher's homepage at www.aspbs.com/ Sudden Cardiac Arrest (SCA) is a devastating heart abnormality which leads to millions of casualty per year. Thus, early detection or prediction of SCA could save the human lives in greater scale. This present work is aimed to predict SCA two minutes before its occurrence and significant results has been obtained using the proposed signal processing methodology. Two international standard databases namely, MIT/BIH Sudden Cardiac Death (SCD) Holter Database for SCA and Physiobank Normal Sinus Rhythm (NSR) for normal control data were used in this work. Initially, five minutes R-R interval of a subject which is two minutes before the onset of SCA was extracted from MIT/BIH database's annotation files for predicting the SCA. Then, Heart Rate Variability (HRV) signal was pre-processed for ectopic beats removal and detrending using mean and discrete wavelet transform (DWT) respectively. Pre-processed HRV was analysed in time, frequency and nonlinear domains to extract various features to efficiently predict SCA. Totally, 34 features (15 time domain, 13 frequency domains, and 6 nonlinear domains) were extracted from each HRV signal samples of normal and SCA subjects. Sequential Feature Selection (SFS) algorithm is used to select optimal features and seven features (2 time, 3 frequency and 2 nonlinear) among 34 features was chosen as a result. Finally, Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) were used to predict the SCA and normal control cases. SVM and PNN give maximum mean SCA prediction rate of 96.36% and 93.64% respectively. Thus the present experimental results clearly indicates that, SVM classifier is more efficient in predicting SCA than PNN and mean classification rate reported in this work is higher compared to the earlier works on predicting SCA. 2015-04-13T02:32:00Z 2015-04-13T02:32:00Z 2014-08 Article Journal of Medical Imaging and Health Informatics, vol. 4(4), 2014, pages 521-532 2156-7018 http://www.ingentaconnect.com/content/asp/jmihi/2014/00000004/00000004/art00006?token=0058173dc858c0c275c277b42573a6766763f2570443a7959592f653b672c57582a72752d70ec9bb54f1f58b http://dspace.unimap.edu.my:80/xmlui/handle/123456789/39430 en American Scientific Publishers
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Analysis of Variance
Heart Rate Variability
Probabilistic Neural Network
Sequential Feature Selection
Sudden Cardiac Arrest
Support Vector Machine
spellingShingle Analysis of Variance
Heart Rate Variability
Probabilistic Neural Network
Sequential Feature Selection
Sudden Cardiac Arrest
Support Vector Machine
Murukesan, L.
Murugappan, Muthusamy, Dr.
Muhammad Nadeem, Iqbal
Krishinan, Saravanan, Dr.
Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features
description Link to publisher's homepage at www.aspbs.com/
author2 murukesan.loganathan23@gmail.com
author_facet murukesan.loganathan23@gmail.com
Murukesan, L.
Murugappan, Muthusamy, Dr.
Muhammad Nadeem, Iqbal
Krishinan, Saravanan, Dr.
format Article
author Murukesan, L.
Murugappan, Muthusamy, Dr.
Muhammad Nadeem, Iqbal
Krishinan, Saravanan, Dr.
author_sort Murukesan, L.
title Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features
title_short Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features
title_full Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features
title_fullStr Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features
title_full_unstemmed Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features
title_sort machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features
publisher American Scientific Publishers
publishDate 2015
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/39430
_version_ 1643799064508104704
score 13.222552

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