Search results for "Arrhythmia organization"

showing 2 items of 2 documents

A method for quantifying atrial fibrillation organization based on wave-morphology similarity

2002

A new method for quantifying the organization of single bipolar electrograms recorded in the human atria during atrial fibrillation (AF) is presented. The algorithm relies on the comparison between pairs of local activation waves (LAWs) to estimate their morphological similarity, and returns a regularity index (/spl rho/) which measures the extent of repetitiveness over time of the detected activations. The database consisted of endocardial data from a multipolar basket catheter during AF and intraatrial recordings during atrial flutter. The index showed maximum regularity (/spl rho/=1) for all atrial flutter episodes and decreased significantly when increasing AF complexity as defined by W…

Signal processingBundle of Hismedicine.medical_specialtyMorphological similarityAtrial fibrillation (AF)Biomedical EngineeringSensitivity and SpecificityPattern Recognition AutomatedElectrocardiographySimilarity (network science)Heart RateInternal medicineAtrial Fibrillationotorhinolaryngologic diseasesmedicineHumansClinical treatmentWaveform morphologyMathematicsmedicine.diagnostic_testMinimum distanceModels CardiovascularReproducibility of ResultsSignal Processing Computer-AssistedAtrial fibrillationEndocardial signalmedicine.diseaseTachyarrhythmia organizationCardiologysense organsRhythm classificationBasket catheterElectrocardiographyAlgorithmsAtrial flutterBiomedical engineeringIEEE Transactions on Biomedical Engineering
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An Automatic System for the Analysis and Classification of Human Atrial Fibrillation Patterns from Intracardiac Electrograms

2008

This paper presents an automatic system for the analysis and classification of atrial fibrillation (AF) patterns from bipolar intracardiac signals. The system is made up of: 1) a feature- extraction module that defines and extracts a set of measures potentially useful for characterizing AF types on the basis of their degree of organization; 2) a feature-selection module (based on the Jeffries-Matusita distance and a branch and bound search algorithm) identifying the best subset of features for discriminating different AF types; and 3) a support vector machine technique-based classification module that automatically discriminates the AF types according to the Wells' criteria. The automatic s…

Signal processingComputer scienceFeature extractionBiomedical EngineeringFeature extraction and selectionFeature selectionSensitivity and SpecificityIntracardiac injectionPattern Recognition AutomatedArtificial IntelligenceSearch algorithmAtrial FibrillationmedicineHumansDiagnosis Computer-AssistedIntracardiac ElectrogramArrhythmia organizationSignal processingmedicine.diagnostic_testbusiness.industrySupport vector machines (SVMs)Reproducibility of ResultsPattern recognitionAtrial fibrillationHuman atrial fibrillationmedicine.diseaseSupport vector machineSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaAutomatic classificationArtificial intelligenceIntracardiac electrogrambusinessElectrocardiographyAlgorithmsIEEE Transactions on Biomedical Engineering
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