ize-Related Properties of Area1 of Approximate Entropy to Characterize Time-series Organization

Natali, J; Starzynski, Paulo Nogueira; El-Dash, Ingird; Luccia, Thiago Paes de Barros De; El-Dash, Vivian; Chauí‐Berlinck, José Guilherme

doi:10.9734/bjast/2016/29596

Cited by 2 publications

(5 citation statements)

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“…Therefore, the scaling profile of the complexity estimator a1ApEn clearly indicates that the cardiac control operates as a pseudo-random system, irrespectively of the metabolic demand (in the range here studied). These results confirm a preliminary analysis of our group [19] and are aligned with recent results and interpretations about cardiac control [24][25][26][27].…”

Section: The Scaling Profilesupporting

confidence: 92%

“…As we show in another study ( [19]), the correlation between a1ApEn values and the size N of vectors can indicate the underlying process that generates the time-series. Briefly, consider the sizes j N where the subscript j indicates the length of a vector (e.g., j = 100, 200, 300, 400, 500).…”

Section: The Scaling Profilementioning

confidence: 73%

“…Briefly, consider the sizes j N where the subscript j indicates the length of a vector (e.g., j = 100, 200, 300, 400, 500). Then, a vector of size S is randomly sub-sampled in v vectors of size j N ( Mean and maximum (Max) is associated to a certain type of process (the reader is advised to see [19] for detailed presentation).…”

Section: The Scaling Profilementioning

confidence: 99%

“…Short series had between 1100 and 1200 R-R intervals. Therefore, in order to guarantee a safe resampling procedure (see [19]), we employed j = 50, 150, 250, 350, 500, and v = 30.…”

Section: The Scaling Profilementioning

confidence: 99%

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Validation of the Area1 of Approximate Entropy (a1ApEn) in Empirical Data of Heart Rate

El-Dash¹,

El-Dash²,

Natali³

et al. 2017

CJAST

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Aims:To validate the use of the non-linear estimator a1ApEn in empirical data. Study Design: Comparison of heart rate variability/complexity (HRV/C) between rest and low intensity exercise. Methodology: R-R intervals were obtained from electrocardiogram recordings in 15 healthy volunteers during 30 minutes of rest followed by 30 minutes of treadmill walking (≅ 4 km/h). The R-R series were linearly detrended, checked for stationarity, and windows of 150 non-overlapping intervals were sequentially extracted. HRV/C estimators were obtained: standard deviation (SDNN), root mean square (RMSSD), power of frequency bands (LF, HF and VHF, i.e., above 0.40 Hz) by STFT, normalized power (nu), a1ApEn. Correlations were studied intra-individual between conditions and intra-population. Additionally, in the Fourier Transform data, phases were randomly shuffled, an inverse transform applied (reconstituted rR-R), and RMSSD and a1ApEn were computed. Finally, the scaling profile of a1ApEn between conditions was addressed. Results: All the HRC/V estimators, except nuLF and nuVHF, showed a decrease in low intensity exercise. For intra-population, all the estimators, except VHF, demonstrated highly significant negative correlations with heart rate. In the reconstituted rR-R series, both RMSSD and a1ApEn

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Section: The Scaling Profilesupporting

confidence: 92%

Section: The Scaling Profilementioning

confidence: 73%

Section: The Scaling Profilementioning

confidence: 99%

“…Short series had between 1100 and 1200 R-R intervals. Therefore, in order to guarantee a safe resampling procedure (see [19]), we employed j = 50, 150, 250, 350, 500, and v = 30.…”

Section: The Scaling Profilementioning

confidence: 99%

See 2 more Smart Citations

Validation of the Area1 of Approximate Entropy (a1ApEn) in Empirical Data of Heart Rate

El-Dash¹,

El-Dash²,

Natali³

et al. 2017

CJAST

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“…For example, Renyi entropy measures of heart rate Gaussianity become a complementary measure of the physiological complexity of the underlying signal transduction processes via robust algorithms 29 ; accurate estimation entropy in very short time series is utilized to detect atrial fibrillation in implanted ventricular devices 30 . The use of a resampling procedure along with the size-related correlations of the nonlinear estimator area1 of approximate entropy provides an effective method to discern different generating processes underlying heart rate time series 31 . Another way of solving these challenges is to properly model the generative mechanism of the time series of heart rate (HR) (60/interbeat intervals).…”

mentioning

confidence: 99%

The control mechanisms of heart rate dynamics in a new heart rate nonlinear time series model

2020

Sci Rep

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the control mechanisms of heart rate dynamics in a new heart rate nonlinear time series model Zonglu He the control mechanisms and implications of heart rate variability (HRV) under the sympathetic (SnS) and parasympathetic nervous system (pnS) modulation remain poorly understood. Here, we establish the HR model/HRV responder using a nonlinear process derived from newton's second law in stochastic self-restoring systems through dynamic analysis of physiological properties. We conduct model validation by testing, predictions, simulations, and sensitivity and time-scale analysis. We confirm that the outputs of the HRV responder can be accepted as the real data-generating process. Empirical studies show that the dynamic control mechanism of heart rate is a stable fixed point, rather than a strange attractor or transitions between a fixed point and a limit cycle; HR slope (amplitude) may depend on the ratio of cardiac disturbance or metabolic demand mean (standard deviation) to myocardial electrical resistance (PNS-SNS activity). For example, when metabolic demands remain unchanged, HR amplitude depends on PNS to SNS activity; when autonomic activity remains unchanged, HR amplitude during resting reflects basal metabolism. HR parameter alterations suggest that age-related decreased HRV, ultrareduced HRV in heart failure, and ultraelevated HRV in St segment alterations refer to age-related decreased basal metabolism, impaired myocardial metabolism, and SnS hyperactivity triggered by myocardial ischemia, respectively.Cardiac disturbances and PNS-SNS restoring force. Heart rate is determined intrinsically by the rate of spontaneous depolarization at the sinoatrial node, but is also modulated by both sympathetic and parasympathetic efferent innervation in response to cardiac disturbances (physical demands, stress, or hormonal factors) 39 . A cardiac disturbance can be driven by an excitatory event, an inhibitory event, or white noise. Excitatory events include acute stress such as low oxygen, high carbon dioxide, ischemia, or hypotension. Inhibitory events include acute stress such as hypertension or certain physiological states such as rest, sleep, comatose, or anesthetic state. Peripheral chemoreceptors located in the aorta, carotid arteries, and the brain are sensory extensions of the peripheral nervous system into blood vessels by which they detect changes in the concentrations of blood borne chemicals and afferent nerves carry them to the brainstem 40 . When baroreceptors located in the carotid sinus and in the aortic arch are excited by a stretch of the blood vessel, they sense the blood pressure changes and relay them to the lower brainstem. The SNS connected to the heart speeds up a slower-than-normal heartbeat by releasing neurohormones known as catecholamines (epinephrine and norepinephrine). The PNS located in the brainstem and upper or sacral portion of the spinal cord slows down a faster-than-normal heartbeat by releasing the neurohormone acetylcholine. The SNS and PNS exerts excitatory and inhibitory effect...

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ize-Related Properties of Area1 of Approximate Entropy to Characterize Time-series Organization

Cited by 2 publications

References 0 publications

Validation of the Area1 of Approximate Entropy (a1ApEn) in Empirical Data of Heart Rate

Validation of the Area1 of Approximate Entropy (a1ApEn) in Empirical Data of Heart Rate

The control mechanisms of heart rate dynamics in a new heart rate nonlinear time series model

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