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Predicting atrial fibrillation inducibility in a canine model by multi-threshold spectra of the recurrence complex network
- Source :
- Medical Engineering & Physics. 35:668-675
- Publication Year :
- 2013
- Publisher :
- Elsevier BV, 2013.
-
Abstract
- The purpose of this study is to predict atrial fibrillation (AF) from epicardial signals by investigating the recurrence property of atrial activity dynamic system before AF. A novel scheme is proposed to predict AF by using multi-threshold spectra of the recurrence complex network. Firstly, epicardial signals are transformed into the recurrence complex network to quantify structural properties of the recurrence in the phase space. Spectral parameters with multi-threshold are used to characterize the global structure of the network. Then the feature sequential forward searching algorithm and mutual information based Maximum Relevance Minimum Redundancy criterion are used to find the optimal feature set. Finally, a support vector machine is used to predict the occurrence of AF. This method is assessed on the pre-AF epicardial signals of canine which includes the normal group A (no further AF will happen), the mild group B (the following AF time is less than 180s) and the severe group C (the following AF time is more than 180s). 25 optimal features are selected out of 180 features from each sample. With these features, sensitivity, specificity and accuracy are 99.40%, 99.70% and 99.60%, respectively, which are the best among the recurrence based methods. The results suggest that the proposed method can predict AF accurately and thus can be prospectively used in the postoperative evaluation.
- Subjects :
- Epicardial Mapping
Male
Support Vector Machine
Biomedical Engineering
Biophysics
Dogs
Search algorithm
Atrial Fibrillation
medicine
Redundancy (engineering)
Animals
Heart Atria
Mathematics
Signal processing
business.industry
Signal Processing, Computer-Assisted
Pattern recognition
Atrial fibrillation
Mutual information
Complex network
medicine.disease
Support vector machine
Disease Models, Animal
Female
Artificial intelligence
business
Canine model
Biomedical engineering
Subjects
Details
- ISSN :
- 13504533
- Volume :
- 35
- Database :
- OpenAIRE
- Journal :
- Medical Engineering & Physics
- Accession number :
- edsair.doi.dedup.....e0027ae188be73fe75de34eb185d31e7