A Neural Network Model for Estimating the Heart Rate Response to Constant Intensity Exercises

Estimating the heart rate (HR) response to exercises of a given intensity without the need of direct measurement is an open problem of great interest. We propose here a model that can estimate the heart rate response to exercise of constant intensity and its subsequent recovery, based on soft computing techniques. Multilayer perceptron artificial neural networks (NN) are implemented and trained using raw HR time series data. Our model’s input and output are the beat-to-beat time intervals and the HR values, respectively. The numerical results are very encouraging, as they indicate a mean relative square error of the estimated HR values of the order of 10−4 and an absolute error as low as 1.19 beats per minute, on average. Our model has also been proven to be superior when compared with existing mathematical models that predict HR values by numerical simulation. Our study concludes that our NN model can efficiently predict the HR response to any constant exercise intensity, a fact that can have many important applications, not only in the area of medicine and cardio-vascular health, but also in the areas of rehabilitation, general fitness, and competitive sport.

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Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data

Journal of The Royal Society Interface ◽

10.1098/rsif.2018.0695 ◽

2018 ◽

Vol 15 (147) ◽

pp. 20180695 ◽

Cited By ~ 7

Author(s):

Simone Cenci ◽

Serguei Saavedra

Keyword(s):

Time Series ◽

Community Dynamics ◽

Time Series Data ◽

Multivariate Time Series ◽

Model Averaging ◽

Biotic Interactions ◽

Series Data ◽

Time Intervals ◽

Novel Approach ◽

Data Generating Process

Biotic interactions are expected to play a major role in shaping the dynamics of ecological systems. Yet, quantifying the effects of biotic interactions has been challenging due to a lack of appropriate methods to extract accurate measurements of interaction parameters from experimental data. One of the main limitations of existing methods is that the parameters inferred from noisy, sparsely sampled, nonlinear data are seldom uniquely identifiable. That is, many different parameters can be compatible with the same dataset and can generalize to independent data equally well. Hence, it is difficult to justify conclusive assertions about the effect of biotic interactions without information about their associated uncertainty. Here, we develop an ensemble method based on model averaging to quantify the uncertainty associated with the effect of biotic interactions on community dynamics from non-equilibrium ecological time-series data. Our method is able to detect the most informative time intervals for each biotic interaction within a multivariate time series and can be easily adapted to different regression schemes. Overall, this novel approach can be used to associate a time-dependent uncertainty with the effect of biotic interactions. Moreover, because we quantify uncertainty with minimal assumptions about the data-generating process, our approach can be applied to any data for which interactions among variables strongly affect the overall dynamics of the system.

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Heart Rate Response to Exercise

Ellestad's Stress Testing ◽

10.1093/med/9780190225483.003.0022 ◽

2018 ◽

pp. 437-445

Author(s):

Gregory S. Thomas

Keyword(s):

Heart Rate ◽

Physical Fitness ◽

Sample Size ◽

Older Persons ◽

Heart Rate Response ◽

Large Sample Size ◽

Maximum Heart Rate ◽

Large Sample ◽

Response To Exercise

The chapter Heart Rate Response to Exercise reviews the studies performed to estimate a patient’s maximum predicted heart rate. While the commonly used formula (220 – age), developed in 1971, is easy to remember, it underestimates the actual maximum heart rate in older persons. Studies of large sample size have found the maximum heart rate to be relatively independent of sex and physical fitness but to incrementally decline with age. The decrease with age is less than 1 beat per minute per year, however. A more accurate and recommended formula is [(208) – (0.7)(age)] as developed by Tanaka and colleagues.

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Assessment of Validity and Reliability of the 1-Minute Sit-to-Stand Test to Measure the Heart Rate Response to Exercise in Healthy Children

JAMA Pediatrics ◽

10.1001/jamapediatrics.2019.1084 ◽

2019 ◽

Vol 173 (7) ◽

pp. 692 ◽

Cited By ~ 3

Author(s):

Gregory Reychler ◽

Nicolas Audag ◽

Natalia Morales Mestre ◽

Gilles Caty

Keyword(s):

Heart Rate ◽

Heart Rate Response ◽

Healthy Children ◽

Validity And Reliability ◽

Stand Test ◽

Sit To Stand ◽

Response To Exercise

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THE HEART RATE RESPONSE TO EXERCISE IN HEALTHY WOMEN AGED 13 TO 88

Southern Medical Journal ◽

10.1097/00007611-199209001-00120 ◽

1992 ◽

Vol 85 (Supplement) ◽

pp. 3S-45

Author(s):

Allen F. Bowyer ◽

Rosemary A. Thomas

Keyword(s):

Heart Rate ◽

Heart Rate Response ◽

Healthy Women ◽

Response To Exercise

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Heart rate control in diabetic rabbits: blunted response to isoproterenol

AJP Endocrinology and Metabolism ◽

10.1152/ajpendo.1988.255.5.e636 ◽

1988 ◽

Vol 255 (5) ◽

pp. E636-E641 ◽

Cited By ~ 3

Author(s):

B. E. Zola ◽

B. Miller ◽

G. L. Stiles ◽

P. S. Rao ◽

E. H. Sonnenblick ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Response ◽

Atropine Sulfate ◽

Left Ventricular ◽

Heart Weight ◽

Base Line ◽

Baroreceptor Reflexes ◽

Diabetic Rabbits ◽

Alloxan Monohydrate ◽

Response To Exercise

To study the effects of chronic diabetes on heart rate and adrenergic responsiveness we compared unanesthetized diabetic rabbits, 10-13 mo after alloxan monohydrate injection, to age-matched controls. There were no significant differences found between groups for body or heart weight. Both resting and intrinsic heart rate (the latter obtained after atropine sulfate and propranolol HCl) were similar. In addition, serum and left ventricular epinephrine and norepinephrine concentrations as well as left ventricular beta-receptor density and affinity were unchanged in diabetic animals. Heart rate responses to isoproterenol were blunted in diabetics at the three highest doses. Base-line mean blood pressure was modestly lower in diabetic rabbits, and parallel declines in pressure for both groups were observed in response to isoproterenol. The diminished heart rate response to isoproterenol in diabetic rabbits may be due to diminished myocardial sensitivity to catecholamines, possibly combined with altered baroreceptor reflexes. These experiments may provide an explanation for the blunted heart rate response to exercise described in human diabetics.

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A New Imputation Method for Treating Missing Precipitation Records

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.635-637.1488 ◽

2014 ◽

Vol 635-637 ◽

pp. 1488-1495

Author(s):

Yu Liu ◽

Feng Rui Chen

Keyword(s):

Time Series Data ◽

Absolute Error ◽

Imputation Method ◽

Temporal Information ◽

Series Data ◽

Precipitation Trend ◽

Monthly Total Precipitation ◽

The Mean ◽

Spatio Temporal ◽

Precipitation Records

This study aims to present a new imputation method for missing precipitation records by fusing its spatio-temporal information. On the basis of extending simple kriging model, a nonstationary kriging method which assumes that the mean or trend is known and varies in whole study area was proposed. It obtains precipitation trend of each station at a given time by analyzing its time series data, and then performs geostatistical analysis on the residual between the trend and measured values. Finally, these spatio-temporal information is integrated into a unified imputation model. This method was illustrated using monthly total precipitation data from 671 meteorological stations of China in April, spanning the period of 2001-2010. Four different methods, including moving average, mean ratio, expectation maximization and ordinary kriging were introduced to compare with. The results show that: Among these methods, the mean absolute error, mean relative error and root mean square error of the proposed method are the smallest, so it produces the best imputation result. That is because: (1) It fully takes into account the spatio-temporal information of precipitation. (2) It assumes that the mean varies in whole study area, which is more in line with the actual situation for rainfall.

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Abnormal heart rate response to exercise in vasospastic angina: Pathophysiologic mechanism in the provocation of coronary spasm

American Heart Journal ◽

10.1016/0002-8703(84)90618-5 ◽

1984 ◽

Vol 108 (2) ◽

pp. 316-326 ◽

Cited By ~ 5

Author(s):

Iwao Sato ◽

Katsuro Shimomura ◽

Yasuhiro Hasegawa ◽

Tohru Ohe ◽

Mokuo Matsuhisa ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Response ◽

Coronary Spasm ◽

Vasospastic Angina ◽

Pathophysiologic Mechanism ◽

Response To Exercise

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Effect of lung transplantation on heart rate response to exercise

Heart & Lung ◽

10.1016/j.hrtlng.2015.01.014 ◽

2015 ◽

Vol 44 (3) ◽

pp. 246-250 ◽

Cited By ~ 1

Author(s):

Hilary F. Armstrong ◽

Jose Gonzalez-Costello ◽

Wilawan Thirapatarapong ◽

Ulrich P. Jorde ◽

Matthew N. Bartels

Keyword(s):

Heart Rate ◽

Lung Transplantation ◽

Heart Rate Response ◽

Response To Exercise

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System analysis of heart rate control in man

Journal of Applied Physiology ◽

10.1152/jappl.1976.41.5.790 ◽

1976 ◽

Vol 41 (5) ◽

pp. 790-796 ◽

Cited By ~ 6

Author(s):

I. Sato ◽

Y. Hasegawa ◽

K. Hotta

Keyword(s):

Heart Rate ◽

Weight Function ◽

Rate Control ◽

Cross Correlation ◽

System Analysis ◽

Heart Rate Response ◽

Control Function ◽

Heart Rate Control ◽

Young Males ◽

Response To Exercise

The dynamic property of the heart rate response to exercise was determined and expressed in the frequency domain to establish a method of examiningcardiovascular control function. The response of heart rate to a stimulus was measured at 5-s intervals in nine healthy young volunteers. The stimulusconsisted of several runs of two-step exercise practiced in semirandom sequence for 19 min. The weight function of the system was estimated from autocorrelation function of the input signal and cross-correlation function between the input and output signals. The weight function was transformed into a transfer function and its Bode plot diagram was drawn. From the diagram, four dynamic parameters were determined. These parameters are as follows: Kis a constant showing the theoretical steady-state increment of heart rate,and T1, T2, T3 are time constants. The values obtained in the present experiment with the healthy young males were: K 46.0 +/- 14.6 beats, T1, 2.12 +/- 0.44, T2, 1.12 +/- 0.16, and T3 0.70 +/- 0.07 min.

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