Search results for "Anesthesiology Monitoring"

showing 2 items of 2 documents

Assessment of regional ventilation distribution: comparison of vibration response imaging (VRI) with electrical impedance tomography (EIT)

2013

BACKGROUND: Vibration response imaging (VRI) is a bedside technology to monitor ventilation by detecting lung sound vibrations. It is currently unknown whether VRI is able to accurately monitor the local distribution of ventilation within the lungs. We therefore compared VRI to electrical impedance tomography (EIT), an established technique used for the assessment of regional ventilation. METHODOLOGY/PRINCIPAL FINDINGS: Simultaneous EIT and VRI measurements were performed in the healthy and injured lungs (ALI; induced by saline lavage) at different PEEP levels (0, 5, 10, 15 mbar) in nine piglets. Vibration energy amplitude (VEA) by VRI, and amplitudes of relative impedance changes (rel.ΔZ) …

Diagnostic ImagingPathologymedicine.medical_specialtyAnatomy and PhysiologyCritical Care and Emergency MedicinePulmonologyVibration Response ImagingSwineRespiratory SystemLung soundlcsh:MedicineVibrationModel OrganismsRespiratory FailureAnesthesiologyBedside TechnologyElectric ImpedancemedicineMedical imagingAnimalsRespiratory Physiologyddc:610lcsh:ScienceTomographyBiologyElectrical impedance tomographyAnesthesiology MonitoringPhysicsAnalysis of VarianceModels StatisticalAnesthesiology TechnologyMultidisciplinarylcsh:RAnimal Modelsrespiratory systemrespiratory tract diseasesPulmonary imagingSpirometryBreathingMedicinelcsh:QTomographyPulmonary VentilationResearch ArticleBiomedical engineering
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Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia

2020

Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are a…

Support Vector MachinePhysiologyComputer scienceElectroencephalographycomputer.software_genreField (computer science)Machine Learning0302 clinical medicineLevel of consciousnessAnesthesiology030202 anesthesiologyMedicine and Health SciencesAnesthesiamedia_commonClinical NeurophysiologyAnesthesiology MonitoringBrain MappingMultidisciplinaryArtificial neural networkmedicine.diagnostic_testPharmaceuticsApplied MathematicsSimulation and ModelingQUnconsciousnessRElectroencephalographyNeuronal pathwayddc:ElectrophysiologyBioassays and Physiological AnalysisBrain ElectrophysiologyAnesthesiaPhysical SciencesEvoked Potentials AuditoryMedicinemedicine.symptomAlgorithmsAnesthetics IntravenousResearch ArticleComputer and Information SciencesConsciousnessImaging TechniquesCognitive NeuroscienceSciencemedia_common.quotation_subjectNeurophysiologyNeuroimagingAnesthesia GeneralResearch and Analysis MethodsBayesian inferenceMachine learningMachine Learning Algorithms03 medical and health sciencesConsciousness MonitorsDrug TherapyArtificial IntelligenceMonitoring IntraoperativeSupport Vector MachinesmedicineHumansMonitoring Physiologicbusiness.industryElectrophysiological TechniquesBiology and Life SciencesSupport vector machineStatistical classificationCognitive ScienceNeural Networks ComputerArtificial intelligenceClinical MedicineConsciousnessbusinesscomputerMathematics030217 neurology & neurosurgeryNeurosciencePLOS ONE
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