Non-invasive real-time autonomic function characterization during surgery via continuous Poincaré quantification of heart rate variability

Abstract

Heart rate variability (HRV) provides an excellent proxy for monitoring of autonomic function, but the clinical utility of such characterization has not been investigated. In a clinical setting, the baseline autonomic function can reflect ability to adapt to stressors such as anesthesia. No monitoring tool has yet been developed that is able to track changes in HRV in real time. This study is a proof-of-concept for a non-invasive, real-time monitoring model for autonomic function via continuous Poincaré quantification of HRV dynamics. Anonymized heart rate data of 18 healthy individuals (18–45 years) undergoing minor procedures and 18 healthy controls (21–35 years) were analyzed. Patients underwent propofol and fentanyl anesthesia, and controls were at rest. Continuous heart rate monitoring was carried out from before aesthetic induction to the end of the surgical procedure. HRV components (sympathetic and parasympathetic) were extracted and analyzed using Poincaré quantification, and a real-time assessment tool was developed. In the patient group, a significant decrease in the sympathetic and parasympathetic components of HRV was observed following anesthesia (SD1: p = 0.019; SD2: p = 0.00027). No corresponding change in HRV was observed in controls. HRV parameters were modelled into a real-time graph. Using the monitoring technique developed, autonomic changes could be successfully visualized in real-time. This could provide the basis for a novel, fast and non-invasive method of autonomic assessment that can be delivered at the point of care. Electronic supplementary material The online version of this article (10.1007/s10877-018-0206-4) contains supplementary material, which is available to authorized users.

Stress and autonomic nerve dysfunction monitoring in perioperative gastric cancer patients using a smart device

“…But interestingly, SDANN was the only HRV parameter that was not significantly affected by anesthesia, indicating that SDANN was quite special, and further demonstrated that SDANN and SDNN were not completely positively correlated. Studies by Mathieu Jeanne, Maddalena Ardissino, and Thomas Ledowski have also demonstrated that anesthesia could result in significant reductions of multiple HRV parameters, which is consistent with our findings (Ardissino et al, 2019 ; Jeanne et al, 2009 ; Ledowski et al, 2005 ). Meanwhile, it was clear that the HRV at POD1 and POD2 is significantly reduced, presented as significant reduction of SDNN and SDANN, indicating patients suffering of a significant postoperative stress at POD1 and POD2, and implicating suppression of autonomic nervous system activity (Garg et al, 2020 ; Hattori & Asamoto, 2020 ; Tracy et al, 2016 ).…”

“…Heart rate variability (HRV) measurements, which were put forward by Hon and Lee for the first time in 1965 (Lee & Hon, 1965 ) are non‐invasive and standardized method to assess the stress response and autonomic nervous function (Ardissino et al, 2019 ; Charlier et al, 2020 ; Mulkey et al, 2020 ). Heart rates fluctuate continuously even at rest and are determined by the discharge cycle of the sinoatrial node.…”

Effect of High-Induction Magnetic Stimulation on Complex Heart Rate Variability of Sus Scrofa Domesticus under General Anesthesia

“…In general, anesthesia is associated with an inhibition of the autonomic nervous system and, consequently, of heart rate variability. Effects of fentanyl and propofol used for anesthesia in this study were repeatedly addressed in patients, and a reduction of overall autonomic nervous system modulation, perhaps with dominant inhibition of sympathetic branch, was found [24][25][26]. However, since in this study, the entire experiment was performed under stable deep anesthesia, we assume that although the absolute levels of heart rate variability may differ between conscious and anesthetized animals, the pattern of HRV changes induced by HIMS should be similar.…”

Telemedical transport layer security based platform for cardiac arrhythmia classification using quadratic time–frequency analysis of HRV signal

“…A high resolution in time and frequency can be achieved by using separable time and frequency smoothing kernel. This yield to the Smoothed Pseudo Wigner-Ville Distribution (SPWVD), which is defined by (5);…”

“…Several studies, based on the analysis of HRV signals, offer a diagnosis aid for arrhythmia detection and characterization. More particularly, time, frequency, time-scale, and time-frequency domain were investigated in several studies for extraction of some specific features [3][4][5][6][7]. Classification techniques, such as Support Vector Machine (SVM), Artificial Neural Networks (ANN) and decision trees, were used to analyze HRV signals for diagnosis aid purposes [8][9][10][11][12].…”