| Paper Details: | Downloads: 461 |
| Serial Number: | P1160649003
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| Title: | Employing Time-Domain Methods and Poincaré Plot of Heart Rate Variability Signals to Detect Congestive Heart Failure
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| Authors: | Alia S. Khaled and Mohamed I. Owis and Abdalla S. A. Mohamed
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| Abstract: | Congestive heart failure (CHF) is a common and serious medical condition where the heart is not able to pump enough blood to meet the body's energy demands. Heart failure typically develops slowly after injury to the heart, such as a heart attack, too much strain on the heart due to years of untreated high blood pressure or diseased cardiac valves. In this study, we consider the problem of detection of CHF using heart rate variability (HRV) analysis techniques, which depend on the variations among consecutive heartbeats. The proposed solution consists of feature extraction followed by a classification step. Features are extracted using HRV analysis methods such as time-domain methods and the Poincaré Plot. For classification, three statistical classifiers and the back-propagation neural networks (BPNN) are used. Results have shown that these classifiers, with normalized features, are capable of detecting CHF with sensitivity of 97.90% and positive predictive accuracy of 98.19%. Time-domain features are capable of discriminating the normal from CHF signals more than Poincaré plot features.
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| Keywords: | HRV, CHF, Time-domain Analysis, Poincaré Plot, Back-propagation Neural Networks.
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| Journal/Conference: | International Journal of Bioinformatics and Medical Engineering
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| Volume: | 6
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| Issue: | 1
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| Submission Date: | 10/1/2006 12:00:00 AM
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| Review Date: | 11/1/2006 12:00:00 AM
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| Publishing Date: | 12/1/2006 12:00:00 AM
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| Article Downloads: | 461
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