Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features
Link to publisher's homepage at www.aspbs.com/
Saved in:
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
American Scientific Publishers
2015
|
| Subjects: | |
| Online Access: | http://dspace.unimap.edu.my:80/xmlui/handle/123456789/39430 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Malaysia Perlis |
| Language: | English |
| id |
my.unimap-39430 |
|---|---|
| record_format |
dspace |
| 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 |