251. Characterization and classification of intracardiac atrial fibrillation signals using the time-singularity multifractal spectrum distribution.
- Author
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Urda-Benitez, Robert D., Castro-Ospina, Andrés E., and Orozco-Duque, Andrés
- Subjects
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ATRIAL fibrillation , *FEATURE extraction , *SUPPORT vector machines , *CATHETER ablation , *ATRIAL arrhythmias , *FRACTAL analysis - Abstract
• Application of time-singularity multifractal spectrum distribution (TS-MFSD) in biomedical signals. • New feature extraction methodology from electrograms based on TS-MFSD. • Classification scheme of EGM signals using only TS-MFSD-based features. The analysis of intracardiac signals, or electrograms (EGM), is one of the most promising tools to guide catheter ablation of atrial fibrillation and improve the success of this procedure. Given the nonlinear nature of EGM signals, several studies have conducted fractal and multifractal analyses to extract nonlinear features related with critical activity. However, the fractal exponent or the multifractal spectrum fail to provide information about the temporal behavior of these signals. To overcome this limitation, the Time-Singularity Multifractal Spectrum Distribution (TS-MFSD) was recently introduced. In this paper, a feature extraction scheme is proposed to compute descriptors from the TS-MFSD that could be used in a classification scheme. The results show that features extracted from the TS-MFSD would serve to classify EGM signals into four classes depending on their level of fragmentation. In addition, the k -Nearest Neighbors and Support Vector Machines classifiers employed in this study, along with the optimal feature subset, achieved an accuracy of 81.37 ± 0.95 % and 83.35 ± 1.04 %, respectively. This finding is comparable with those of other works that have used features based on the morphology of local activation waves and amplitude thresholds. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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