AI Article Synopsis

  • This review discusses the noninvasive evaluation of cardiac autonomic nervous system activity (ANSA) through easily accessible parameters like heart rate (HR) and heart-rate variability (HRV), highlighting their importance in risk assessment for sudden cardiac death.
  • The translation of these parameters from controlled lab settings to real-world ambulatory environments faces challenges due to various uncontrolled factors such as blood pressure changes and physical activity.
  • To improve the accuracy of ANSA evaluations in everyday settings, the authors suggest using a multi-sensor and multiparametric approach, along with data fusion and machine-learning techniques.

Article 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.

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http://dx.doi.org/10.1016/j.jelectrocard.2024.153837DOI Listing

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Article Synopsis
  • This review discusses the noninvasive evaluation of cardiac autonomic nervous system activity (ANSA) through easily accessible parameters like heart rate (HR) and heart-rate variability (HRV), highlighting their importance in risk assessment for sudden cardiac death.
  • The translation of these parameters from controlled lab settings to real-world ambulatory environments faces challenges due to various uncontrolled factors such as blood pressure changes and physical activity.
  • To improve the accuracy of ANSA evaluations in everyday settings, the authors suggest using a multi-sensor and multiparametric approach, along with data fusion and machine-learning techniques.
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