Tracking autonomic nervous system activity using surface ECG: Personalized, multiparametric evaluation

Vladimir Shusterman; Cees A. Swenne; Stacy Hoffman; Patrick J. Strollo; Barry London

doi:10.1016/j.jelectrocard.2024.153837

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Tracking autonomic nervous system activity using surface ECG: Personalized, multiparametric evaluation

Journal article

Peer reviewed

Tracking autonomic nervous system activity using surface ECG: Personalized, multiparametric evaluation

Vladimir Shusterman, Cees A. Swenne, Stacy Hoffman, Patrick J. Strollo and Barry London

Journal of electrocardiology, Vol.88, 153837

01/2025

DOI: 10.1016/j.jelectrocard.2024.153837

PMCID: PMC11717603

PMID: 39615267

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https://pmc.ncbi.nlm.nih.gov/articles/PMC11717603/pdf/nihms-2039248.pdfView

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Abstract

We present a concise review of the background, pitfalls, and potential solutions for the noninvasive evaluation and continuous tracking of cardiac autonomic nervous system activity (ANSA), using surface-ECG-accessible parameters, including heart rate (HR), heart-rate variability (HRV), and cardiac repolarization. These parameters have provided insights into the dynamics of cardiac ANSA in controlled experiments and have proved useful in risk assessment with respect to sudden cardiac death and all-cause mortality in some patient populations, as well as in implantable device programming. Yet attempts to translate these parameters from the laboratory environment to ambulatory settings have been hampered by the presence of multiple uncontrolled factors, including changes in blood pressure, body position, physical activity, and respiration frequency. We show that a single-parameter-based, simplified cardiac ANSA evaluation in an uncontrolled ambulatory setting could be inaccurate, and we discuss several approaches to improve accuracy. Discerning cardiac ANSA effects in uncontrolled ambulatory environments requires tracking multiple physiological processes, preferably using multisensor, multiparametric monitoring and controlling some physiological variables (e.g., respiration frequency); data fusion and machine-learning-based analytics are instrumental for developing more accurate personalized ANSA evaluation.

Autonomic nervous system

Cardiac repolarization variability

Cardiac rhythm monitoring

Heart rate variability

Wearable cardiovascular devices

Details

Title: Subtitle: Tracking autonomic nervous system activity using surface ECG: Personalized, multiparametric evaluation
Creators: Vladimir Shusterman - Division of Cardiovascular Medicine, The University of Iowa, Iowa City, IA, United States of America
Cees A. Swenne - Leiden University Medical Center
Stacy Hoffman - PinMed (United States)
Patrick J. Strollo - University of Pittsburgh
Barry London - University of Iowa
Resource Type: Journal article
Publication Details: Journal of electrocardiology, Vol.88, 153837
DOI: 10.1016/j.jelectrocard.2024.153837
PMID: 39615267
PMCID: PMC11717603
NLM abbreviation: J Electrocardiol
ISSN: 0022-0736
eISSN: 1532-8430
Publisher: Elsevier Inc
Grant note: NIH: HL0771160, HL114277
Several studies presented in the manuscript were supported in part by NIH grants HL0771160 and HL114277 (PI: Dr. V. Shusterman).
Language: English
Electronic publication date: 11/2024
Date published: 01/2025
Academic Unit: Molecular Physiology and Biophysics; Cardiovascular Medicine; Internal Medicine
Record Identifier: 9984751756002771

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