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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Rahman, Q.A. Tereshchenko, L.G. Kongkatong, M. Abraham, T. Abraham, M.R. Shatkay, H. |
| Copyright Year | 2014 |
| Description | Author affiliation: Comput. Biol. & Machine Learning Lab., Queen's Univ., Kingston, ON, Canada (Rahman, Q.A.; Shatkay, H.) || Heart & Vascular Inst., Johns Hopkins Univ., Baltimore, MD, USA (Tereshchenko, L.G.; Kongkatong, M.; Abraham, T.; Abraham, M.R.) |
| Abstract | Test based on electrocardiograms (ECG) that record the heart electrical activity can help in early detection of patients with hypertrophic cardiomyopathy (HCM) where the heart muscle is partially thickened and blood flow is (potentially fatally) obstructed. This paper presents a cardiovascular-patient classifier we developed to identify HCM patients using standard 10-seconds, 12-lead ECG signals. Patients are classified as having HCM if the majority of the heartbeats are recognized as HCM. Thus, the classifier's underlying task is to recognize individual heartbeats segmented from 12-lead ECG signals as HCM beats, where heartbeats from non-HCM cardiovascular patients are used as controls. We extracted 504 morphological and temporal features - both commonly used and newly-developed ones-from ECG signals for heartbeat classification. To assess classification performance, we trained and tested a random forest classifier and a support vector machine classifier using 5-fold cross validation. The patient-classification precision of both classifiers are close to 0.85. Recall (sensitivity) and specificity are approximately 0.90. We also conducted feature selection experiments by gradually removing the least informative features; the results show that a relatively small subset of 304 highly informative features can achieve performance measures comparable to that achieved by using the complete set of features. |
| Starting Page | 224 |
| Ending Page | 229 |
| File Size | 354671 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479956692 |
| DOI | 10.1109/BIBM.2014.6999159 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-02 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Sensitivity Feature selection Heart beat Patient classification Electrocardiogram Machine learning methods Electrocardiography Feature extraction Standards Hypertrophic Cardiomyopathy |
| Content Type | Text |
| Resource Type | Article |
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