Classification of intracavitary electrograms in atrial fibrillation using information and complexity measures
| Autor: | Jonathan José Carlos Nicolet, Juan F. Restrepo, Gastón Schlotthauer |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Radiofrequency ablation Tsallis entropy 0206 medical engineering Biomedical Engineering Health Informatics 02 engineering and technology INGENIERÍAS Y TECNOLOGÍAS Information theory Approximate entropy law.invention Rényi entropy 03 medical and health sciences ATRIAL FIBRILLATION 0302 clinical medicine law medicine Entropy (information theory) COMPLEXITY MEASURES Permutation entropy Ingeniería Médica business.industry Atrial fibrillation Pattern recognition INFORMATION THEORY Otras Ingeniería Médica medicine.disease 020601 biomedical engineering COMPLEX FRACTIONATED ATRIAL ELECTROGRAMS Signal Processing Artificial intelligence business 030217 neurology & neurosurgery |
| Popis: | Background Classification of complex fractionated atrial electrograms is crucial for the study of atrial fibrillation and the development of treatment strategies, because these electrophysiological phenomena represent a common substrate for radiofrequency ablation in treatment of this arrythmia.ObjectiveThe objective of this work is the characterization of short term atrial electrograms using nonlinear dynamics measures, helping in the automatic classification of electrograms.MethodsThe dataset consists of 113 atrial electrograms recordings from left-atrial endocardial mapping. These signals were classified by three expert electrophysiologists into four classes, from C0 (non fractionated) to C3 (high degree of fractionation). The calculated features were Approximate entropy, Dispersion entropy, Fuzzy entropy, Permutation entropy, Tsallis entropy, Shannon entropy, Renyi entropy, and Lempel-Ziv complexity. Features were selected for classification using Neighborhood Component Analysis. Different classifiers were tested using selected features, and the one with maximum sensitivity and specificity in each task was reported.ResultsWe obtained a classification performance that overcome previous works on this database and are comparable to the results of studies performed over bigger datasets. Separation between C3 signals from (C0, C1, C2) signals was performed with 99.98% sensitivity and 96.61% specificity. Non-fractionated signals (C0 + C1) were separated from fractionated signals (C2 + C3) with 96.72% sensitivity and 94.51% specificity. Moreover, the estimation times of the selected features are low enough to consider the online application of this scheme.Conclusions and significanceClassification performance obtained using information and complexity measures shown better results than previous works over this dataset, encouraging the application of these features to characterize atrial electrograms. Fil: Nicolet, Jonathan José Carlos. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática; Argentina Fil: Restrepo Rinckoar, Juan Felipe. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática; Argentina Fil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática; Argentina |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|