0000000000363188

AUTHOR

Francisco Castells

showing 8 related works from this author

Atrial activity extraction for atrial fibrillation analysis using blind source separation.

2004

This contribution addresses the extraction of atrial activity (AA) from real electrocardiogram (ECG) recordings of atrial fibrillation (AF). We show the appropriateness of independent component analysis (ICA) to tackle this biomedical challenge when regarded as a blind source separation (BSS) problem. ICA is a statistical tool able to reconstruct the unobservable independent sources of bioelectric activity which generate, through instantaneous linear mixing, a measurable set of signals. The three key hypothesis that make ICA applicable in the present scenario are discussed and validated: 1) AA and ventricular activity (VA) are generated by sources of independent bioelectric activity; 2) AA …

Computer scienceSpeech recognitionHeart VentriclesBiomedical EngineeringSignalBlind signal separationSensitivity and SpecificityElectrocardiographyRobustness (computer science)Heart Conduction SystemAtrial FibrillationmedicineHumansDiagnosis Computer-AssistedHeart AtriaPrincipal Component Analysismedicine.diagnostic_testBody Surface Potential MappingContrast (statistics)Reproducibility of ResultsAtrial fibrillationmedicine.diseaseIndependent component analysisKurtosisElectrocardiographyAlgorithmsIEEE transactions on bio-medical engineering
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Bioelectric model of atrial fibrillation: Applicability of blind source separation techniques for atrial activity estimation in atrial fibrillation e…

2003

In this contribution, we present the theoretical justification that give support to the suitability of blind signal separation (BSS) techniques for the estimation of the atrial activity (AA) present in ECGs of persistent atrial fibrillation (AF). The application of BSS methods to this problem needs the fulfillment of several conditions regarding AA, ventricular activity (VA) and the fashion in which both activities arise on the body surface, that will be justified along the paper. To empirically validate the model, an ICA method is applied to 10 real 12-lead recordings of AF. The identification of AA is put forward based on kurtosis and spectral analysis. The kurtosis value of the estimated…

medicine.diagnostic_testComputer scienceSpeech recognitionPersistent atrial fibrillationKurtosismedicineSpectral analysisAtrial fibrillationmedicine.diseaseElectrocardiographyBlind signal separationAlgorithm
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Wavelet variance differences in atrial fibrillation during anaesthetic effect

2008

Effect of anaesthetic agents in restoration rhythm procedures during atrial fibrillation (AF) has not been fully investigated. We evaluated the effects of a widely used anaesthetic agent (propofol) in the fibrillation patterns. Intra-atrial recordings belong to 18 patients diagnosed with AF were analyzed ldquobeforerdquo (baseline) and ldquoduringrdquo anaesthetic infusion. The goal of this study is to characterize the variation in atrial properties along the atria in both states. The wavelet variance decomposes the variance of a time series on a scale by scale basis and hence has considerable appeal when physical phenomena are analyzed in terms of variations operating over a range of diffe…

Fibrillationmedicine.diagnostic_testbusiness.industryWavelet transformAtrial fibrillationmedicine.diseaseHaar waveletRhythmWaveletAnesthesiacardiovascular systemmedicinemedicine.symptombusinessPropofolElectrocardiographymedicine.drug2008 Computers in Cardiology
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Analysis of inter-atrium differences in paroxysmal and persistent atrial fibrillation using principal component analysis

2007

The pathophysiological mechanisms of atrial fibrillation (AF) are not entirely clear yet, and there is no full explanation for the development and evolution of the arrhythmia. The goal of this study is to find inter-atrium differences in electrophysiological behavior between persistent and paroxysmal AF. The database analyzed contains intra-cardiac records from 14 patients with paroxysmal AF and 10 with persistent AF. Dominant frequency and sample entropy measurements showed that in the paroxysmal group there was a left-to-right gradient. These differences were enhanced after the extraction of the main components with principal component analysis. These findings may be interpreted as a poss…

medicine.medical_specialtymedicine.diagnostic_testbusiness.industryLeft atriumAtrial fibrillationDominant frequencymedicine.diseaseSample entropymedicine.anatomical_structureAnesthesiaInternal medicinePersistent atrial fibrillationPrincipal component analysiscardiovascular systemmedicineCardiologybusinessElectrocardiographyParoxysmal AF2007 Computers in Cardiology
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Left ventricular Myocardial dysfunction in arrhythmogenic cardiomyopathy with left ventricular involvement: A door to improving diagnosis.

2018

[EN] Background: Diagnostic Task Force Criteria (TFC) for arrhythmogenic cardiomyopathy (AC) exhibit poor performance for left dominant forms. TFC only include right ventricular (RV) dysfunction (akinesia, dyssynchrony, volumes and ejection fraction). Moreover, cardiac magnetic resonance imaging (CMRI) assessment of left ventricular (LV) dyssynchrony has hitherto not been described. Thus, we aimed to comprehensively characterize LVCMRI behavior in AC patients. Methods: Thirty-five AC patients with LV involvement and twenty-three non-affected family members (controls) were enrolled. Feature-tracking analysis was applied to cine CMRI to assess LV ejection fraction (LVEF), LV end-systolic and …

AdultMalemedicine.medical_specialtyHeart VentriclesCardiomyopathyMagnetic Resonance Imaging Cine030204 cardiovascular system & hematologyVentricular Function LeftStrainTECNOLOGIA ELECTRONICA03 medical and health sciencesVentricular Dysfunction Left0302 clinical medicineCardiac magnetic resonance imagingDiastoleInternal medicinemedicineLate gadolinium enhancementHumansCor030212 general & internal medicineLeft ventricular involvementVentricular dysfunctionCardiac magnetic resonance imagingArrhythmogenic Right Ventricular DysplasiaEjection fractionmedicine.diagnostic_testTask forcebusiness.industryLeft ventricular arrhythmogenic cardiomyopathyReproducibility of ResultsStroke VolumeMiddle Agedmedicine.diseasePatologiaDyssynchronyLv dyssynchronyCardiologyFemaleCardiology and Cardiovascular MedicineLEFT DOMINANTbusiness
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Poincaré surface profile. Novel non-invasive method to detect preferential ventricular response during atrial fibrillation

2007

The strategy of rate control during atrial fibrillation (AF) essentially deals with efforts to utilize and adjust the filtering properties of the atrioventricular (AV) node, allowing AF to continue and ensure that ventricular rate is controlled. In this work, a new tool based on the 3D Poincare plots is developed for the characterization of the ventricular response (VR) and the clinical evaluation of rate control therapies. Mechanisms underlying atrioventricular conduction during AF remain unclear. The role of the dual pathway AV nodal electrophysiology and the effects of the AV node modifications are still being an incognita. RR interval clusters exhibiting harmonic behaviour have been qua…

medicine.medical_specialtymedicine.diagnostic_testAtrioventricular conductionNon invasiveRR intervalRate controlAtrial fibrillationmedicine.diseaseElectrophysiologyInternal medicinecardiovascular systemmedicineCardiologycardiovascular diseasesElectrocardiographyDual pathwayMathematics2007 Computers in Cardiology
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From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-versus-rest classification strategy

2022

Abstract Objective. Detecting different cardiac diseases using a single or reduced number of leads is still challenging. This work aims to provide and validate an automated method able to classify ECG recordings. Performance using complete 12-lead systems, reduced lead sets, and single-lead ECGs is evaluated and compared. Approach. Seven different databases with 12-lead ECGs were provided during the PhysioNet/Computing in Cardiology Challenge 2021, where 88 253 annotated samples associated with none, one, or several cardiac conditions among 26 different classes were released for training, whereas 42 896 hidden samples were used for testing. After signal preprocessing, 81 features per ECG-le…

Machine LearningElectrocardiographyHeart DiseasesPhysiologyPhysiology (medical)Atrial FibrillationBiomedical EngineeringBiophysicsHumansSignal Processing Computer-AssistedInfermeria cardiovascularPhysiological Measurement
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An open access database for the evaluation of heart sound algorithms

2016

In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases…

EngineeringResearch groupsDatabases FactualPhysiologySpeech recognition0206 medical engineeringphonocardiogram (PCG)Biomedical EngineeringBiophysicsMEDLINE02 engineering and technologycomputer.software_genreArticleheart soundAccess to InformationTECNOLOGIA ELECTRONICACoronary artery diseasePhysioNet/CinC Challenge[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingPhysiology (medical)heart sound classification0202 electrical engineering electronic engineering information engineeringmedicineHumansSegmentationHeart valveSound (geography)databasePhonocardiogramgeographygeography.geographical_feature_categoryDatabasebusiness.industryPhonocardiographySignal Processing Computer-Assistedmedicine.disease020601 biomedical engineeringHeart Soundsmedicine.anatomical_structureheart sound segmentationHeart sounds020201 artificial intelligence & image processingbusinessAlgorithmcomputerAlgorithms
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