Abstract |
Feature extraction is a crucial aspect of computer-aided arrhythmia diagnosis using an electrocardiogram (ECG). A location, width and magnitude (LWM) model is proposed for extracting each wave's features in the ECG. The model is a stream of Gaussian function in which three parameters (the expected value, variance and amplitude) are applied to approximate the P wave, QRS wave and T wave. Moreover, the features such as the P-Q intervals, S-T intervals, and so on are easily obtained. Then, a mixed approach is presented for estimating the parameters of a real ECG signal. To illustrate this model's associated advantages, the extracted parameters combined with R-R intervals are fed to three classifiers for arrhythmia diagnoses. Two kinds of arrhythmias, including the premature ventricular contraction ( PVC) heartbeats and the atrial premature complexes (APC) heartbeats, are diagnosed from normal beats using the data from the MIT-BIH arrhythmia database. The results in this study demonstrate that using these parameters results in more accurate and universal arrhythmia diagnoses.
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Authors | Junjiang Zhu, Lingsong He, Zhiqiang Gao |
Journal | Bio-medical materials and engineering
(Biomed Mater Eng)
Vol. 24
Issue 6
Pg. 2883-91
( 2014)
ISSN: 1878-3619 [Electronic] Netherlands |
PMID | 25226994
(Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
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Topics |
- Algorithms
- Arrhythmias, Cardiac
(diagnosis)
- Artificial Intelligence
- Computer Simulation
- Diagnosis, Computer-Assisted
(methods)
- Electrocardiography
(methods)
- Humans
- Models, Cardiovascular
- Models, Statistical
- Pattern Recognition, Automated
(methods)
- Reproducibility of Results
- Sensitivity and Specificity
- Ventricular Premature Complexes
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