Abstract
A classic method to evaluate autonomic dysfunction is through the evaluation of heart rate variability (HRV). HRV provides a series of coefficients, such as Standard Deviation of n-n intervals (SDNN) and Root Mean Square of Successive Differences (RMSSD), which have well-established physiological associations. However, using only electrocardiogram (ECG) signals, it is difficult to identify proper autonomic activity, and the standard techniques are not sensitive and robust enough to distinguish pure autonomic modulation in heart dynamics from cardiac dysfunctions. In this proof-of-concept study we propose the use of Poincaré mapping and Recurrence Quantification Analysis (RQA) to identify and characterize stochasticity and chaoticity dynamics in ECG recordings. By applying these non-linear techniques in the ECG signals recorded from a set of Parkinson’s disease (PD) animal model 6-hydroxydopamine (6-OHDA), we showed that they present less variability in long time epochs and more stochasticity in short-time epochs, in their autonomic dynamics, when compared with those of the sham group. These results suggest that PD animal models present more “rigid heart rate” associated with “trembling ECG” and bradycardia, which are direct expressions of Parkinsonian symptoms. We also compared the RQA factors calculated from the ECG of animal models using four computational ECG signals under different noise and autonomic modulatory conditions, emulating the main ECG features of atrial fibrillation and QT-long syndrome.
Highlights
The autonomic nervous system (ANS) modulates cardiovascular function via two main pathways, the sympathetic (SNS) and parasympathetic (PNS) systems that play agonist-antagonist roles (Cannon, 1939)
To assess the autonomic dysfunction associated with heart rate dynamics, we propose a proof-of-concept study where we constructed a set of four artificial ECG patterns modeling the main ECG features related to the two most common autonomic-cardiac dysfunctions, atrial fibrillation (AF) and long-QT syndrome (QT), and two control ECG signals, a complete periodic regular ECG (DET) activity without noise, and an ECG pattern with high Gaussian noise (GN)
To show the possible differences using traditional coefficients and Recurrence Quantification Analysis (RQA), we first evaluated the dynamics associated with the four artificial ECG patterns by varying the percentage of noise on their heart rate variability (HRV) with four different low frequency (LF)/high frequency (HF) ratios
Summary
The autonomic nervous system (ANS) modulates cardiovascular function via two main pathways, the sympathetic (SNS) and parasympathetic (PNS) systems that play agonist-antagonist roles (Cannon, 1939). Traditional techniques quantify autonomic modulations searching for frequency characteristics, namely, low frequency (LF), high frequency (HF), and their ratio LF/HF ranges or temporal features, standard deviation of NN intervals (SDNN), and root mean square of successive R-R interval differences (RMSSD), on the ECG-tachogram along the time series constructed (most commonly) from the R-R peak time distances (RR: interbeat intervals between all successive heartbeats; NN: interbeat intervals from which artifacts have been removed) (Malliani et al, 1994; Montano et al, 2009; Akselrod et al, 2014). These methods have already been applied to ANS dysfunction related to seizures and sudden death, revealing their capacity to characterize biosignals in a clinical context (Zbilut and Webber, 1992; Marwan et al, 2002; Marwan, 2003; Billeci et al, 2018; Khazaei et al, 2018)
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