Heartbeat Time Series Classification With Support Vector Machines
In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing l...
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| Published in | IEEE transactions on information technology in biomedicine Vol. 13; no. 4; pp. 512 - 518 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.07.2009
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1089-7771 1558-0032 1558-0032 |
| DOI | 10.1109/TITB.2008.2003323 |
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| Abstract | In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation. The performance of the SVM with respect to other state-of-the-art classifiers is also confirmed by the classification of signals presenting very low signal-to-noise ratio. Finally, the influence of the number of features to the classification rate was also investigated for two real datasets. The first dataset consists of long-term ECG recordings of young and elderly healthy subjects. The second dataset consists of long-term ECG recordings of normal subjects and subjects suffering from coronary artery disease. |
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| AbstractList | In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation. The performance of the SVM with respect to other state-of-the-art classifiers is also confirmed by the classification of signals presenting very low signal-to-noise ratio. Finally, the influence of the number of features to the classification rate was also investigated for two real datasets. The first dataset consists of long-term ECG recordings of young and elderly healthy subjects. The second dataset consists of long-term ECG recordings of normal subjects and subjects suffering from coronary artery disease.In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation. The performance of the SVM with respect to other state-of-the-art classifiers is also confirmed by the classification of signals presenting very low signal-to-noise ratio. Finally, the influence of the number of features to the classification rate was also investigated for two real datasets. The first dataset consists of long-term ECG recordings of young and elderly healthy subjects. The second dataset consists of long-term ECG recordings of normal subjects and subjects suffering from coronary artery disease. In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation. The performance of the SVM with respect to other state-of-the-art classifiers is also confirmed by the classification of signals presenting very low signal-to-noise ratio. Finally, the influence of the number of features to the classification rate was also investigated for two real datasets. The first dataset consists of long-term ECG recordings of young and elderly healthy subjects. The second dataset consists of long-term ECG recordings of normal subjects and subjects suffering from coronary artery disease. |
| Author | Kampouraki, A. Manis, G. Nikou, C. |
| Author_xml | – sequence: 1 givenname: A. surname: Kampouraki fullname: Kampouraki, A. organization: Dept. of Comput. Sci., Univ. of Ioannina, Ioannina, Greece – sequence: 2 givenname: G. surname: Manis fullname: Manis, G. organization: Dept. of Comput. Sci., Univ. of Ioannina, Ioannina, Greece – sequence: 3 givenname: C. surname: Nikou fullname: Nikou, C. organization: Dept. of Comput. Sci., Univ. of Ioannina, Ioannina, Greece |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19273030$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Adult Aged Aged, 80 and over Algorithms Artificial Intelligence Coronary Artery Disease - physiopathology Electrocardiography Feature extraction Heart beat Heart Rate - physiology heart rate variability (HRV) heartbeat time series Humans Male Models, Statistical Neural networks Senior citizens Signal analysis Signal Processing, Computer-Assisted Signal to noise ratio Statistical analysis support vector machine (SVM) Support vector machine classification Support vector machines |
| Title | Heartbeat Time Series Classification With Support Vector Machines |
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