Search results for "Heart"

showing 10 items of 3201 documents

Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?

2021

Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize well. Unfortunately, the process of having a large number of manually curated images by medical experts is both slow and utterly expensive. In this paper, we set out to explore whether expert knowledge is a strict requirement for the creation of annotated data sets on which machine learning can successfully be trained. To do so, we gauged the performance of three segmentation models, namely U-Net, Attention U-Net, and ENet, trained with dif…

FOS: Computer and information sciencesComputer Science - Machine LearningComputer scienceProcess (engineering)GeneralizationIndustrial engineering. Management engineeringComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern Recognitionheartannotated data setT55.4-60.8Machine learningcomputer.software_genre030218 nuclear medicine & medical imagingTheoretical Computer ScienceMachine Learning (cs.LG)Set (abstract data type)03 medical and health sciences0302 clinical medicineFOS: Electrical engineering electronic engineering information engineeringSegmentationNumerical AnalysisArtificial neural networkbusiness.industryDeep learningsegmentationImage and Video Processing (eess.IV)deep learningQA75.5-76.95Electrical Engineering and Systems Science - Image and Video ProcessingComputational MathematicsHausdorff distanceComputational Theory and MathematicsIndex (publishing)Electronic computers. Computer scienceArtificial intelligencebusinesscomputer030217 neurology & neurosurgeryMRI
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A rule‐based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts

2019

Rule-based methods are often used for assigning fiber orientation to cardiac anatomical models. However, existing methods have been developed using data mostly from the left ventricle. As a consequence, fiber information obtained from rule-based methods often does not match histological data in other areas of the heart such as the right ventricle, having a negative impact in cardiac simulations beyond the left ventricle. In this work, we present a rule-based method where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the endocardium of the right ventricle, the inter…

FOS: Computer and information sciencesmedicine.medical_specialtyHeart VentriclesBiomedical EngineeringFOS: Physical sciencesVolume mesh030204 cardiovascular system & hematology[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]030218 nuclear medicine & medical imagingComputational Engineering Finance and Science (cs.CE)03 medical and health sciences0302 clinical medicineRule-based methodInternal medicine[INFO.INFO-IM]Computer Science [cs]/Medical ImagingmedicineHumansComputer SimulationElectrophysiological simulationsInterventricular septumOutflow tractComputer Science - Computational Engineering Finance and ScienceMolecular BiologyEndocardiumFiber (mathematics)Orientation (computer vision)MyocardiumApplied MathematicsFiber orientationOutflow tract ventricular arrhythmiaModels CardiovascularRule-based systemSeptumMagnetic Resonance Imaging[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationPhysics - Medical PhysicsElectrophysiological Phenomenamedicine.anatomical_structureComputational Theory and MathematicsVentricleModeling and Simulationcardiovascular systemCardiologyOutflowMedical Physics (physics.med-ph)SoftwareGeologyInternational Journal for Numerical Methods in Biomedical Engineering
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Multiscale partial information decomposition of dynamic processes with short and long-range correlations: theory and application to cardiovascular co…

2022

Abstract Objective. In this work, an analytical framework for the multiscale analysis of multivariate Gaussian processes is presented, whereby the computation of Partial Information Decomposition measures is achieved accounting for the simultaneous presence of short-term dynamics and long-range correlations. Approach. We consider physiological time series mapping the activity of the cardiac, vascular and respiratory systems in the field of Network Physiology. In this context, the multiscale representation of transfer entropy within the network of interactions among Systolic arterial pressure (S), respiration (R) and heart period (H), as well as the decomposition into unique, redundant and s…

FOS: Computer and information sciencesmultivariate time seriesPhysiologyEntropyRespirationBiomedical EngineeringBiophysicsheart rate variabilitytransfer entropyredundancy and synergyBlood PressureHeartQuantitative Biology - Quantitative MethodsCardiovascular SystemMethodology (stat.ME)Heart RatePhysiology (medical)FOS: Biological sciencesCardiovascular controlSettore ING-INF/06 - Bioingegneria Elettronica E Informaticavector autoregressive fractionally integrated (VARFI) modelsHumansQuantitative Methods (q-bio.QM)Statistics - MethodologyPhysiological measurement
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Remote heart rate variability for emotional state monitoring

2018

International audience; Several researches have been conducted to recognize emotions using various modalities such as facial expressions , gestures, speech or physiological signals. Among all these modalities, physiological signals are especially interesting because they are mainly controlled by the autonomic nervous system. It has been shown for example that there is an undeniable relationship between emotional state and Heart Rate Variability (HRV). In this paper, we present a methodology to monitor emotional state from physiological signals acquired remotely. The method is based on a remote photoplethysmography (rPPG) algorithm that estimates remote Heart Rate Variability (rHRV) using a …

Facial expressionModalities[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceSpeech recognition020208 electrical & electronic engineering0206 medical engineering[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]02 engineering and technology[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020601 biomedical engineeringSignal[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingFeature (computer vision)Frequency domainPhotoplethysmogram0202 electrical engineering electronic engineering information engineeringHeart rate variabilityGesture
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Mass spectral identification of the blocked N-terminal tryptic peptide of the ATPase inhibitor from beef heart mitochondria

1984

AbstractThe presence of a formyl blocking group at the N-terminus of the ATPase inhibitor has been identified and the partial sequence of the N-terminal peptide has been determined by fast atom bombardment and field desorption coupled to mass spectrometry. Minor discrepancies in amino acid sequence of the inhibitor between the present and published data [(1981) Proc. Natl. Acad. Sci. USA 78, 7403-7407] are reported and its relationships with other inhbitors are briefly discussed.

Fast atom bombardmentATPaseBiophysicsPeptideN-formyi blocking groupSaccharomyces cerevisiaeMass spectrometryBiochemistryMass SpectrometryMitochondria HeartSpecies SpecificityStructural BiologyEndopeptidasesGeneticsAnimalsTrypsinAmino Acid SequenceMolecular BiologyPeptide sequencechemistry.chemical_classificationBeef heart mitochondriabiologyChemistryTryptic peptideProteinsCell BiologyFast atom bombardmentField desorption Amino acid sequenceATPase inhibitorPeptide FragmentsMitochondriaProton-Translocating ATPasesBiochemistrybiology.proteinCattleFEBS Letters
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Classification of Heart Sounds Using Convolutional Neural Network

2020

Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global averag…

Feature engineeringComputer science0206 medical engineeringconvolutional neural networkneuroverkot02 engineering and technologyOverfittingConvolutional neural networklcsh:Technologylcsh:Chemistry0202 electrical engineering electronic engineering information engineeringFeature (machine learning)General Materials ScienceSensitivity (control systems)sydäntauditInstrumentationlcsh:QH301-705.5Fluid Flow and Transfer Processesbusiness.industrylcsh:TProcess Chemistry and TechnologyDeep learning020208 electrical & electronic engineeringGeneral EngineeringPattern recognitiondiagnostiikkaMatthews correlation coefficientautomatic heart sound classification020601 biomedical engineeringlcsh:QC1-999Computer Science Applicationsfeature engineeringkoneoppiminenlcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040Heart soundsArtificial intelligencetiedonlouhintabusinesslcsh:Engineering (General). Civil engineering (General)lcsh:PhysicsApplied Sciences
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Assessing causality in brain dynamics and cardiovascular control

2013

Understanding how different cerebral areas interact to produce an integrated behaviour and disentangling the mechanisms that contribute to cardiovascular control are two of the major challenges of brain and cardiovascular neuroscience. The increasing availability of simultaneous continuous

Feedback PhysiologicalCognitive scienceIntroductionComputer scienceGeneral MathematicsGeneral EngineeringBrainGeneral Physics and AstronomyHeartCardiovascular controlModels BiologicalCausality (physics)EngineeringPhysics and AstronomyMathematics; Engineering; Physics and AstronomySettore ING-INF/06 - Bioingegneria Elettronica E InformaticaAnimalsHumansComputer SimulationNerve NetAlgorithmsMathematic
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Differences in tissue distribution of iron from various clinically used intravenous iron complexes in fetal avian heart and liver.

2015

Abstract Nanomedicines are more complex than most pharmacologically active substances or medicines and have been considered as non-biological complex drugs. For nanomedicines pivotal pharmacokinetic properties cannot be assessed by plasma concentration data from standard bioequivalence studies. Using intravenous iron complexes (IICs) as model we show that fetal avian tissues can be used to study time dependent tissue concentrations in heart and liver. Clear differences were found between equimolar doses of sucrose, gluconate or carboxymaltose coated iron particles. The range in tissue iron concentrations observed with these clinically widely used IICs provides an orientation as to what shou…

FetusExperimental modelbusiness.industryIronTissue ironAuthorizationIntravenous ironHeartGeneral MedicineBioequivalencePharmacologyToxicologyNanomedicineLiverTherapeutic EquivalencyPharmacokineticsAnimalsDrugs GenericMedicineAdministration IntravenousTissue DistributionTissue distributionbusinessChickensIron CompoundsRegulatory Toxicology and Pharmacology
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Non-immune hydrops fetalis: Two case reports

2021

BACKGROUND Fetal hydrops is a serious condition difficult to manage, often with a poor prognosis, and it is characterized by the collection of fluid in the extravascular compartments. Before 1968, the most frequent cause was the maternal-fetal Rh incompatibility. Today, 90% of the cases are non-immune hydrops fetalis. Multiple fetal anatomic and functional disorders can cause non-immune hydrops fetalis and the pathogenesis is incompletely understood. Etiology varies from viral infections to heart disease, chromosomal abnormalities, hematological and autoimmune causes. CASE SUMMARY A 38-year-old pregnant woman has neck lymphoadenomegaly, fever, cough, tonsillar plaques at 14 wk of amenorrhea…

Fetusmedicine.medical_specialtyAmniotic fluidHeart diseaseObstetricsbusiness.industryAnemiaFetal transfusionFetal anemiaGeneral Medicinemedicine.diseaseHydrops fetalisPreterm cesarean sectionBlood chemistryInfectious disease (medical specialty)Hydrops fetalisCase reportmedicineEtiologybusinessCordocentesisWorld Journal of Clinical Cases
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Non-syndromic Mitral Valve Dysplasia Mutation Changes the Force Resilience and Interaction of Human Filamin A

2018

International audience; Filamin A (FLNa), expressed in endocardial endothelia during fetal valve morphogenesis, is key in cardiac development. Missense mutations in FLNa cause non-syndromic mitral valve dysplasia (FLNA-MVD). Here, we aimed to reveal the currently unknown underlying molecular mechanism behind FLNA-MVD caused by the FLNa P637Q mutation. The solved crystal structure of the FLNa3-5 P637Q revealed that this mutation causes only minor structural changes close to mutation site. These changes were observed to significantly affect FLNa's ability to transmit cellular force and to interact with its binding partner. The performed steered molecular dynamics simulations showed that signi…

Filamins[SDV]Life Sciences [q-bio]Protein Tyrosine Phosphatase Non-Receptor Type 12Heart Valve DiseasesMutation MissenseMorphogenesisProtein tyrosine phosphataseMolecular Dynamics SimulationBiologyFilaminta3111ArticleFLNA-MVD03 medical and health sciencessteered molecular dynamics simulationsStructural Biologymechanical forcesmedicineHumansMitral valve prolapseMissense mutationFLNAmolekyylidynamiikkasydäntauditCell adhesionMolecular Biology030304 developmental biologyX-ray crystallography0303 health sciencesBinding Sites030302 biochemistry & molecular biologyta1182filamiinitprotein tyrosine phosphatase 12medicine.disease3. Good healthCell biologyFilamin AMutation (genetic algorithm)cardiovascular systemMitral Valveproteiinitmitral valve prolapseröntgenkristallografiaProtein Binding
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