Juan Manuel Martín-González

Heart rate dynamics in different levels of Zen meditation.

The dynamic interactions among physiological rhythms imbedded in the heart rate signal can give valuable insights into autonomic modulation in conditions of reduced outward attention. Therefore, in this study we analyzed the heart rate variability (HRV) in different levels of practice in Zen meditation (Zazen). Nineteen subjects with variable experience took part in this study. In four special cases we collected both HRV and respiration data. The time series were analyzed in frequency domain and also using the Continuous Wavelet Transform, which detects changes in the time domain and in the frequency domain simultaneously. The shifts in the respiratory modulation of heart rate, or respiratory sinus arrhythmia (RSA), reflect the different levels of practice among practitioners with variable experience in Zazen; in turn the modulation of the RSA may reflect changes in the breathing pattern as in the parasympathetic outflow related to the quality and focus of attention in each stage.

Heart rate variability during high-intensity exercise

The aim of this paper is to describe and analyse the behaviour of heart rate variability (HRV) during constant-load, high-intensity exercise using a time frequency analysis (Wavelet Transform). Eleven elite cyclists took part in the study (age: 18.6±3.0 years; VO2max: 4.88±0.61 litres·min−1). Initially, all subjects performed an incremental cycloergometer test to determine load power in a constant load-test (379.55±36.02 W; 89.0%). HRV declined dramatically from the start of testing (p <0.05). The behaviour of power spectral density within the LF band mirrored that of total energy, recording a significant decrease from the outset LF peaks fell rapidly thereafter, remaining stable until the end of the test. HF-VHF fell sharply in the first 20 to 30 seconds. The relative weighting (%) of HF-VHF was inverted with the onset of fatigue, [1.6% at the start, 7.1 (p <0.05) at the end of the first phase, and 43.1% (p <0.05) at the end of the test]. HF-VHFpeak displayed three phases: a moderate initial increase, followed by a slight fall, thereafter increasing to the end of the test. The LF/HF-VHF ratio increased at the start, later falling progressively until the end of the first phase and remaining around minimal values until the end of the test.

Does mindfulness meditation shift the cardiac autonomic nervous system to a highly orderly operational state

The relative impactofmeditationon autonomic nervous system(ANS) regulation of heart rate variability (HRV) is probably specific according to the technique and practice time. The purpose of the present study is to determine how different levels of expertise in mindfulness meditation, otherwise being characterized by an extraordinary (effortless) attentive state to the presentmoment, affect HRV. In a previous communication [1] we found evidences that different practice time among Zen meditators reflect stage-specific patterns of cardiac variability related to attention regulation processes that are different in each stage. In the present work we analyzed the same data (here we also collected data from oneMaster of Vipassana meditation) using a robust methodology specially designed to investigate the time-scale and the dynamic properties of the signal. A total of 20 subjects (7 females and 13 males, mean age 43.65± 7.60 years, meditation experience ranging between 2 months and 35 years (9.81±8.82 years)) gave their written consent (in accordance with the Helsinki Declaration) to participate in the study. RR interval data were collected by using Polar S810i (Polar Electro Oy, Finland). Participants were instructed to seat quietly for 10–15 min (cross-legged on a cushion) and then meditate for 30–40 min. Data were recorded 2–4 times in each subject during a month retreat in a Zen monastery. HRV data were analyzed using the discrete wavelet transform (DWT) (Appendix A), which filters the series to obtain the wavelet coefficients at different scales [2]. At each decomposition level J, the approximation coefficients (lowpass) cAj and detailed coefficients (highpass) cD1, cD2,...,cDj are obtained, and we can reconstruct the approximation signal Aj(t) and the detail signals Dj(t), j=1...J. We extracted the normalized wavelet variance in each scale (level J=6) using the DWT for all HRV time series collected during meditation. An exploratory principal component analysis was conducted on the correlation matrix of these HRV indices. In addition, we analyzed binary sequences representing the acceleration and deceleration of heartbeats (symbolic analysis — Appendix B). The irregularities (complexity) of the binary sequences were quantified in a statistical manner using Approximate Entropy (ApEn) [4–7]. Table 1 shows the correlation coefficients for the first two principal components (PCs) corresponding to the original variables (wavelet coefficients) and the explained variance (the proportion to which each PC accounts for the variability in the data). Note that for a better interpretation of the results we defined the ‘scale index’ 1–6, which was compared to the standard frequency intervals defined by the Task Force (i) very low frequency (VLF), ≤0.04 Hz, (ii) low frequency (LF), 0.04–0.15 Hz, and (iii) high frequency (HF), 0.15–0.4 Hz [8]. The dataset distributionwhen using a scatter plot of the first two PCs reveals three apparent cluster structures (Fig. 1). Samples ‘located’ in the right edge of the space defined by the PCs (higher positive scores in the PC1) are strongly marked by variability of both VLF and HF oscillations (scales 1 and 6), presenting an opposite behavior to that of samples representedwith theblack color,whopresent thevariance centered in the LF range (scales 3 and 4). Remarkably, the samples in the right edge of the plot correspond to the less-experienced practitioners in thewhole group, while the black samples correspond to very long-term meditators, including both Masters of Zen and Vipassana. Fig. 2 shows the medians of the variance in each scale for the three clusters identified in Fig.1. Note thatmore experiencedpractitioners (both black and gray) decrease variability in the HF range (scales 5 and 6).

Effect of Hyperbaric Pressure During Scuba Diving on Autonomic Modulation of the Cardiac Response: Application of the Continuous Wavelet Transform to the Analysis of Heart Rate Variability

This study sought to determine the effects of hyperbaric pressure on heart rate modulation, by analyzing potential changes in heart rate variability (HRV). Ten divers were exposed to pressures of 1, 2, 3, and 4 atmospheres absolute (ATA). The test was performed in a hyperbaric chamber. Heart rate (HR) was recorded in supine subjects for 10 minutes per atmosphere. HRV was analyzed in the frequency mode (fast-Fourier transform and continuous wavelet transform). Results confirmed bradycardia as pressure increased. The drop in HR attained statistical significance after 2, 3, and 4 ATA. Signal energy (normalized TP values) rose progressively, becoming significant at 2 ATA. High frequency and low frequency displayed similar behavior in both cases. Although frequency band peaks did not yield clear results, continuous wave transform analysis showed that the frequency spectrum tended to shift into the high-frequency range as pressure increased. In summary, increased pressure prompted increased bradycardia, and HRV shifted into high-frequency range.

Wavelet Transforms in Sport: Application to Biological Time Series

In sports, biological signals are often used to control and design the sports activity. One of the most common used signals is heart rate (HR). Heart rate variability (HRV) refers to natural fluctuations in the interval between normal heartbeats that occurs while individuals rest or exercise. HRV results from the dynamic interplay between the multiple physiologic mechanisms that regulate HR, and it mainly reflects an expression of the interplay between the sympathetic and parasympathetic nervous systems (Task Force, 1996). Two main oscillatory processes interact with the heart as feedback and forward mechanisms, via autonomic pathways: the modulation of the heart rate by breathing, known as respiratory sinus arrhythmia (RSA), and the short-term blood pressure control, known as baroreflex. These main rhythms usually appear in the high and low frequency ranges of HRV, respectively; however, the dynamic interactions in the cardiovascular system may change this typical spectrum. However, the intrinsic properties of the complex autonomic regulation of cardiovascular function are difficult to measure since, even at rest, emotions and mental loading may affect it.

Análise da resposta HRV em uma prova de esforço incremental: análise tempo-freqüência

Resumen pt: Introducao: Este estudo se propoe a descrever o comportamento da variabilidade do ritmo cardiaco (HRV) no dominio da frequencia, durante a realizacao de ...