TY - GEN
T1 - Identifying hypertrophic cardiomyopathy patients by classifying individual heartbeats from 12-lead ECG signals
AU - Rahman, Quazi Abidur
AU - Tereshchenko, Larisa G.
AU - Kongkatong, Matthew
AU - Abraham, Theodore
AU - Abraham, M. Roselle
AU - Shatkay, Hagit
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/29
Y1 - 2014/12/29
N2 - 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.
AB - 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.
KW - Electrocardiogram
KW - Feature selection
KW - Hypertrophic Cardiomyopathy
KW - Machine learning methods
KW - Patient classification
UR - http://www.scopus.com/inward/record.url?scp=84922787239&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84922787239&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2014.6999159
DO - 10.1109/BIBM.2014.6999159
M3 - Conference contribution
AN - SCOPUS:84922787239
T3 - Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
SP - 224
EP - 229
BT - Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
A2 - Zheng, Huiru
A2 - Hu, Xiaohua Tony
A2 - Berrar, Daniel
A2 - Wang, Yadong
A2 - Dubitzky, Werner
A2 - Hao, Jin-Kao
A2 - Cho, Kwang-Hyun
A2 - Gilbert, David
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
Y2 - 2 November 2014 through 5 November 2014
ER -