Quantifying rapid changes in cardiovascular state with a moving ensemble average

Abstract

MEAP, the moving ensemble analysis pipeline, is a new open-source tool designed to perform multisubject preprocessing and analysis of cardiovascular data, including electrocardiogram (ECG), impedance cardiogram (ICG), and continuous blood pressure (BP). In addition to traditional ensemble averaging, MEAP implements a moving ensemble averaging method that allows for the continuous estimation of indices related to cardiovascular state, including cardiac output, preejection period, heart rate variability, and total peripheral resistance, among others. Here, we define the moving ensemble technique mathematically, highlighting its differences from fixed-window ensemble averaging. We describe MEAP's interface and features for signal processing, artifact correction, and cardiovascular-based fMRI analysis. We demonstrate the accuracy of MEAP's novel B point detection algorithm on a large collection of hand-labeled ICG waveforms. As a proof of concept, two subjects completed a series of four physical and cognitive tasks (cold pressor, Valsalva maneuver, video game, random dot kinetogram) on 3 separate days while ECG, ICG, and BP were recorded. Critically, the moving ensemble method reliably captures the rapid cyclical cardiovascular changes related to the baroreflex during the Valsalva maneuver and the classic cold pressor response. Cardiovascular measures were seen to vary considerably within repetitions of the same cognitive task for each individual, suggesting that a carefully designed paradigm could be used to capture fast-acting event-related changes in cardiovascular state.

Automatic analysis of pre‐ejection period during sleep using impedance cardiogram

“…The reliability of PEP measurements depends on the accurate detection of B point on ICG signal (Árbol et al, 2017;Cieslak et al, 2017;Debski et al, 1991;Debski, Zhang, Jennings, & Kamarck, 1993;Ermishkin, Kolesnikov, & Lukoshkova, 2014;Lozano et al, 2007;Miller & Horvath, 1978;Sherwood et al, 1990;Stern, Wolf, & Belz, 1985;van Lien, Schutte, Meijer, & de Geus, 2013). However, the detection of B point, defined as a reversal, inflection, or rapid slope change on the ICG signal rise, can be challenging as it can be easily affected by noise and artifacts mainly caused by body movements and electrode displacements.…”

Toward a better noninvasive assessment of preejection period: A novel automatic algorithm for B‐point detection and correction on thoracic impedance cardiogram

“…Unlike the data-driven model between ECG R-peak and dZ/dt B- and C-points (Lozano, et al, 2007), the proposed algorithm is not sensitive to the noise and artifacts that alter the location of the C-point or R-peak. Unlike the machine learning methods (Cieslak, et al, 2017), the proposed algorithm does not require a training dataset representing all possible patterns of B-point. Compared to the time-frequency (Wang, Sun, & Van de Water, 1995) and wavelet analysis (Wang, Sun, & Van de Water, 1995) methods, the proposed algorithm has less complexity and is less sensitive to noise and artifacts that have overlapping frequency content with that of the dZ/dt around B-point.…”

Sympathetic and parasympathetic involvement in time constrained sequential foraging

“…2) -pre-ejection period (PEP), high frequency heart rate variability (HF HRV) and heart rate (HR). Semi-automated software MEAP labeled the continuous ECG and ICG (Cieslak et al, 2018). For each heartbeat, the ECG R point serves as the t=0 landmark for within-heartbeat events.…”

“…Here, we employ state of the art cardiac analyses (Barbieri et al, 2005;Cieslak et al, 2018) on electrocardiogram (ECG) and impedance cardiogram (ICG) data recorded continuously while subjects performed a prey selection task, capturing trialwise modulation of sympathetic and parasympathetic contributions of the autonomic state. Using these trialwise indices, we address three questions; (1) how drive in the different stress systems aligns with choice policy and responds to changes in environmental richness; (2) how activation in these systems correlate with learning parameters; and (3) if activity in either autonomic system is associated with optimal task performance.…”