Document Detail


Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal.
MedLine Citation:
PMID:  21709338     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Atrial fibrillation (AF) is the most common cardiac arrhythmia and increases the risk of stroke. Predicting the onset of paroxysmal AF (PAF), based on noninvasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize risks for the patients. In this paper, we propose an effective PAF predictor which is based on the analysis of the RR-interval signal. This method consists of three steps: preprocessing, feature extraction and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the RR-interval signal is extracted. In the next step, the recurrence plot (RP) of the RR-interval signal is obtained and five statistically significant features are extracted to characterize the basic patterns of the RP. These features consist of the recurrence rate, length of longest diagonal segments (L(max )), average length of the diagonal lines (L(mean)), entropy, and trapping time. Recurrence quantification analysis can reveal subtle aspects of dynamics not easily appreciated by other methods and exhibits characteristic patterns which are caused by the typical dynamical behavior. In the final step, a support vector machine (SVM)-based classifier is used for PAF prediction. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which consists of both 30 min ECG recordings that end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, positive predictivity and negative predictivity were 97%, 100%, 100%, and 96%, respectively. The proposed methodology presents better results than other existing approaches.
Authors:
Maryam Mohebbi; Hassan Ghassemian
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-6-27
Journal Detail:
Title:  Physiological measurement     Volume:  32     ISSN:  1361-6579     ISO Abbreviation:  -     Publication Date:  2011 Jun 
Date Detail:
Created Date:  2011-6-28     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9306921     Medline TA:  Physiol Meas     Country:  -    
Other Details:
Languages:  ENG     Pagination:  1147-1162     Citation Subset:  -    
Affiliation:
Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
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