Quantifying rapid changes in cardiovascular state with a moving ensemble average

Autor: Scott T. Grafton, Jim Blascovich, William S. Ryan, Wendy Meiring, Robert M. Kelsey, Zoe M. Rathbun, Matthew Cieslak, Hannah Erro, Viktoriya Babenko
Rok vydání: 2017
Předmět:
Valsalva Maneuver
Multifunction cardiogram
Cognitive Neuroscience
medicine.medical_treatment
Ensemble averaging
Blood Pressure
Experimental and Cognitive Psychology
Cardiography
Impedance
050105 experimental psychology
Electrocardiography
Young Adult
03 medical and health sciences
0302 clinical medicine
Developmental Neuroscience
Heart Rate
medicine
Valsalva maneuver
Humans
Heart rate variability
0501 psychology and cognitive sciences
Cardiac Output
Video game
Biological Psychiatry
Artifact (error)
medicine.diagnostic_test
Endocrine and Autonomic Systems
business.industry
General Neuroscience
05 social sciences
Cold pressor test
Signal Processing
Computer-Assisted
Pattern recognition
Baroreflex
Impedance cardiography
Neuropsychology and Physiological Psychology
Neurology
Female
Artificial intelligence
business
Psychology
Algorithms
030217 neurology & neurosurgery
Zdroj: Psychophysiology. 55
ISSN: 1469-8986
0048-5772
Popis: MEAP, the moving ensemble analysis pipeline, is a new open-source tool designed to perform multisubject preprocessing and analysis of cardiovascular data, including electrocardiogram (ECG), impedance cardiogram (ICG), and continuous blood pressure (BP). In addition to traditional ensemble averaging, MEAP implements a moving ensemble averaging method that allows for the continuous estimation of indices related to cardiovascular state, including cardiac output, preejection period, heart rate variability, and total peripheral resistance, among others. Here, we define the moving ensemble technique mathematically, highlighting its differences from fixed-window ensemble averaging. We describe MEAP's interface and features for signal processing, artifact correction, and cardiovascular-based fMRI analysis. We demonstrate the accuracy of MEAP's novel B point detection algorithm on a large collection of hand-labeled ICG waveforms. As a proof of concept, two subjects completed a series of four physical and cognitive tasks (cold pressor, Valsalva maneuver, video game, random dot kinetogram) on 3 separate days while ECG, ICG, and BP were recorded. Critically, the moving ensemble method reliably captures the rapid cyclical cardiovascular changes related to the baroreflex during the Valsalva maneuver and the classic cold pressor response. Cardiovascular measures were seen to vary considerably within repetitions of the same cognitive task for each individual, suggesting that a carefully designed paradigm could be used to capture fast-acting event-related changes in cardiovascular state.
Databáze: OpenAIRE
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