Heart rate variability (HRV; variability of the RR interval of the electrocardiogram) results from the activity of several coexisting control mechanisms, which involve the influence of respiration (RESP) and systolic blood pressure (SBP) oscillations operating across multiple temporal scales and changing in different physiological states. In this study, multiscale information decomposition is used to dissect the physiological mechanisms related to the genesis of HRV in 78 young volunteers monitored at rest and during postural and mental stress evoked by head-up tilt (HUT) and mental arithmetics (MA). After representing RR, RESP and SBP at different time scales through a recently proposed method based on multivariate state space models, the joint information transfer T RESP , SBP → RR is decomposed into unique, redundant and synergistic components, describing the strength of baroreflex modulation independent of respiration ( U SBP → RR ), nonbaroreflex ( U RESP → RR ) and baroreflex-mediated ( R RESP , SBP → RR ) respiratory influences, and simultaneous presence of baroreflex and nonbaroreflex respiratory influences ( S RESP , SBP → RR ), respectively. We find that fast (short time scale) HRV oscillations-respiratory sinus arrhythmia-originate from the coexistence of baroreflex and nonbaroreflex (central) mechanisms at rest, with a stronger baroreflex involvement during HUT. Focusing on slower HRV oscillations, the baroreflex origin is dominant and MA leads to its higher involvement. Respiration influences independent on baroreflex are present at long time scales, and are enhanced during HUT.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515015PMC
http://dx.doi.org/10.3390/e21050526DOI Listing

Publication Analysis

Top Keywords

resp sbp
16
sbp →
16
multiscale decomposition
8
control mechanisms
8
heart rate
8
rate variability
8
time scales
8
respiratory influences
8
baroreflex nonbaroreflex
8
resp
6

Similar Publications

Background: Previous studies have documented the effectiveness of music therapy in improving adverse neonatal outcomes in premature infants. However, this review aims to address the question of how long listening to music can enhance these neonatal outcomes.

Methods: To conduct this dose-response meta-analysis, we searched the PubMed, Scopus, Web of Science, and Cochrane Library databases.

View Article and Find Full Text PDF

Objectives: To develop and validate a simplified Bleeding Audit Triage Trauma (sBATT) score for use by lay persons, or in areas and environments where physiological monitoring equipment may be unavailable or inappropriate.

Design: The sBATT was derived from the original BATT, which included prehospital systolic blood pressure (SBP), heart rate, respiratory rate, Glasgow Coma Scale (GCS), age and trauma mechanism. Variables suitable for lay interpretation without monitoring equipment were included (age, level of consciousness, absence of radial pulse, tachycardia and trapped status).

View Article and Find Full Text PDF

Background: Air pollution (AP) has become a substantial environmental issue affecting human cardiorespiratory health. Physical exercise (PE) is widely accepted to promote cardiorespiratory health. There is a paucity of research on the point at which the level of polluted environment engaged in PE could be used as a preventive approach to compensate for the damages of AP.

View Article and Find Full Text PDF

An arterial spin labeling-based radiomics signature and machine learning for the prediction and detection of various stages of kidney damage due to diabetes.

Front Endocrinol (Lausanne)

December 2024

National Health Commission (NHC) Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.

Article Synopsis
  • The study aims to evaluate how well a radiomics signature from arterial spin labeling (ASL) imaging can predict and detect kidney damage in diabetic patients and to find imaging risk factors for early renal injury.
  • The research involved three groups: healthy volunteers, diabetic individuals with mild kidney issues (microalbuminuria), and patients with severe diabetic nephropathy, using advanced MRI techniques to analyze kidney texture features.
  • A variety of machine learning models were employed to create predictive tools that can identify early kidney injury and monitor its progression, linking the radiomics features to biological indicators of kidney health.
View Article and Find Full Text PDF
Article Synopsis
  • Heart rate variability (HRV) has potential as a tool for assessing the severity of hemorrhagic shock (HS) by comparing it with traditional methods of measuring hemodynamic and metabolic parameters.
  • The study involved male Sprague-Dawley rats subjected to varying degrees of HS, revealing that HRV changes, particularly in low-frequency power and respiratory sinus arrhythmia, correlate with blood pressure and tissue perfusion.
  • Despite observed HRV alterations being linked to hemodynamic factors, they didn’t show a direct connection with inflammatory responses in the animals.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!