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

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.