Modeling Heart Rate Dynamics in Response to Changes in Exercise Intensity

2015 ◽  
Author(s):  
Michael J. Mazzoleni ◽  
Claudio L. Battaglini ◽  
Brian P. Mann
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Heart Rate ◽  
Heart Rate Response ◽  
Synthetic Data ◽  
Model Parameters ◽  
Nonlinear Mathematical Model ◽  
Heart Rate Dynamics ◽  
Parameter Values ◽  
Good Agreement

This paper develops a nonlinear mathematical model to describe the heart rate response of an individual during cycling. The model is able to account for the fluctuations of an individual’s heart rate while they participate in exercise that varies in intensity. A method for estimating the model parameters using a genetic algorithm is presented and implemented, and the results show good agreement between the actual parameter values and the estimated values when tested using synthetic data.

Coefficient Identification of Steam Network Model Based on Genetic Algorithm

2013 ◽  
Vol 658 ◽  
pp. 555-559
Author(s):  
Xiao Feng Zhu ◽  
Yong Zhang ◽  
Zhao Feng Lu ◽  
Yong Ma
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Network Model ◽  
Model Parameters ◽  
The Real ◽  
Coefficient Identification ◽  
Model Based ◽  
Test Result

This article establish a coupled thermo-hydraulic mathematical model for steam network by adopting a set of equations. Here, identification is defined as process in which a number of Steam Network model parameters are adjusted until the model mimics behavior of the real Steam Network as closely as possible. Test result indicates the advantage of genetic algorithm.

Modeling and Control of an Electro-Hydraulic Poppet Valve

2004 ◽  
Author(s):  
Patrick Opdenbosch ◽  
Nader Sadegh ◽  
Wayne J. Book
Keyword(s):  
Mathematical Model ◽  
Control Valve ◽  
Model Parameters ◽  
Hydraulic Control ◽  
Nonlinear Mathematical Model ◽  
Modeling And Control ◽  
Poppet Valve ◽  
Control Scheme ◽  
Experimental Approaches ◽  
And Control

This paper explores the dynamic modeling of a novel two stage bidirectional poppet valve and proposes a control scheme that uses a Nodal Link Perceptron Network (NLPN). The dynamic nonlinear mathematical model of this Electro-Hydraulic Control Valve (EHCV) is based on the analysis of the interactions among its mechanical, hydraulic, and electromagnetic subsystems. A discussion on experimental approaches to determine the model parameters is included along with model validation results. Finally, the control scheme is developed by proposing that the states of the EHCV follow a set of desired states, which are calculated based upon the desired valve flow conductance coefficient KV. A simulation is presented at the end to verify the proposed control scheme.

Modeling baroreflex regulation of heart rate during orthostatic stress

2006 ◽  
Vol 291 (5) ◽  
pp. R1355-R1368 ◽  
Author(s):  
Mette S. Olufsen ◽  
Hien T. Tran ◽  
Johnny T. Ottesen ◽  
Lewis A. Lipsitz ◽  
Vera Novak
Keyword(s):  
Blood Pressure ◽  
Mathematical Model ◽  
Heart Rate ◽  
Hysteresis Loop ◽  
Elderly People ◽  
Elderly Subjects ◽  
Orthostatic Stress ◽  
Postural Change ◽  
Heart Rate Regulation ◽  
Heart Rate Dynamics

During orthostatic stress, arterial and cardiopulmonary baroreflexes play a key role in maintaining arterial pressure by regulating heart rate. This study presents a mathematical model that can predict the dynamics of heart rate regulation in response to postural change from sitting to standing. The model uses blood pressure measured in the finger as an input to model heart rate dynamics in response to changes in baroreceptor nerve firing rate, sympathetic and parasympathetic responses, vestibulo-sympathetic reflex, and concentrations of norepinephrine and acetylcholine. We formulate an inverse least squares problem for parameter estimation and successfully demonstrate that our mathematical model can accurately predict heart rate dynamics observed in data obtained from healthy young, healthy elderly, and hypertensive elderly subjects. One of our key findings indicates that, to successfully validate our model against clinical data, it is necessary to include the vestibulo-sympathetic reflex. Furthermore, our model reveals that the transfer between the nerve firing and blood pressure is nonlinear and follows a hysteresis curve. In healthy young people, the hysteresis loop is wide, whereas, in healthy and hypertensive elderly people, the hysteresis loop shifts to higher blood pressure values, and its area is diminished. Finally, for hypertensive elderly people, the hysteresis loop is generally not closed, indicating that, during postural change from sitting to standing, baroreflex modulation does not return to steady state during the first minute of standing.

scholarly journals A mathematical model of ventilation response to inhaled carbon monoxide

2005 ◽  
Vol 98 (6) ◽  
pp. 2033-2044 ◽  
Author(s):  
James H. Stuhmiller ◽  
Louise M. Stuhmiller
Keyword(s):  
Mathematical Model ◽  
Carbon Monoxide ◽  
Time History ◽  
Oxygen Metabolism ◽  
Physiological Model ◽  
Model Parameters ◽  
Control Mechanisms ◽  
Animal Data ◽  
Good Agreement

A comprehensive mathematical model, describing the respiration, circulation, oxygen metabolism, and ventilatory control, is assembled for the purpose of predicting acute ventilation changes from exposure to carbon monoxide in both humans and animals. This Dynamic Physiological Model is based on previously published work, reformulated, extended, and combined into a single model. Model parameters are determined from literature values, fitted to experimental data, or allometrically scaled between species. The model predictions are compared with ventilation-time history data collected in goats exposed to carbon monoxide, with quantitatively good agreement. The model reaffirms the role of brain hypoxia on hyperventilation during carbon monoxide exposures. Improvement in the estimation of total ventilation, through a more complete knowledge of ventilation control mechanisms and validated by animal data, will increase the accuracy of inhalation toxicology estimates.

Validation of a Mathematical Model for Road Cycling Power

1998 ◽  
Vol 14 (3) ◽  
pp. 276-291 ◽  
Author(s):  
James C. Martin ◽  
Douglas L. Milliken ◽  
John E. Cobb ◽  
Kevin L. McFadden ◽  
Andrew R. Coggan
Keyword(s):  
Mathematical Model ◽  
Measurement System ◽  
Standard Error ◽  
Power Measurement ◽  
Model Parameters ◽  
Model Parameter ◽  
Road Cycling ◽  
Highly Correlated ◽  
Parameter Values ◽  
Measured Power

This investigation sought to determine if cycling power could be accurately modeled. A mathematical model of cycling power was derived, and values for each model parameter were determined. A bicycle-mounted power measurement system was validated by comparison with a laboratory ergometer. Power was measured during road cycling, and the measured values were compared with the values predicted by the model. The measured values for power were highly correlated (R2= .97) with, and were not different than, the modeled values. The standard error between the modeled and measured power (2.7 W) was very small. The model was also used to estimate the effects of changes in several model parameters on cycling velocity. Over the range of parameter values evaluated, velocity varied linearly (R2> .99). The results demonstrated that cycling power can be accurately predicted by a mathematical model.

MODELING SELECTION AND EXTINCTION MECHANISMS OF BIOLOGICAL SYSTEMS

2011 ◽  
Vol 22 (07) ◽  
pp. 669-686 ◽  
Author(s):  
ADIL AMIRJANOV
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Analytical Solution ◽  
Biological Systems ◽  
Selection Mechanism ◽  
Evolutionarily Stable Strategies ◽  
Animal Group ◽  
Theoretical Predictions ◽  
Evolutionarily Stable ◽  
Good Agreement

In this paper, the behavior of a genetic algorithm is modeled to enhance its applicability as a modeling tool of biological systems. A new description model for selection mechanism is introduced which operates on a portion of individuals of population. The extinction and recolonization mechanism is modeled, and solving the dynamics analytically shows that the genetic drift in the population with extinction/recolonization is doubled. The mathematical analysis of the interaction between selection and extinction/recolonization processes is carried out to assess the dynamics of motion of the macroscopic statistical properties of population. Computer simulations confirm that the theoretical predictions of described models are in good approximations. A mathematical model of GA dynamics was also examined, which describes the anti-predator vigilance in an animal group with respect to a known analytical solution of the problem, and showed a good agreement between them to find the evolutionarily stable strategies.

scholarly journals Investigation on Evolutionary Computation Techniques of a Nonlinear System

2011 ◽  
Vol 2011 ◽  
pp. 1-21 ◽  
Author(s):  
Tran Trong Dao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Simulated Annealing ◽  
Nonlinear System ◽  
Dynamic Behaviour ◽  
Batch Process ◽  
Nonlinear Mathematical Model ◽  
Artificial Intelligent ◽  
Simulation And Optimization ◽  
System A

The main aim of this work is to show that such a powerful optimizing tool like evolutionary algorithms (EAs) can be in reality used for the simulation and optimization of a nonlinear system. A nonlinear mathematical model is required to describe the dynamic behaviour of batch process; this justifies the use of evolutionary method of the EAs to deal with this process. Four algorithms from the field of artificial intelligent—differential evolution (DE), self-organizing migrating algorithm (SOMA), genetic algorithm (GA), and simulated annealing (SA)—are used in this investigation. The results show that EAs are used successfully in the process optimization.

scholarly journals Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography

2022 ◽  
Vol 12 ◽  
Author(s):  
Nicholas Mattia Marazzi ◽  
Giovanna Guidoboni ◽  
Mohamed Zaid ◽  
Lorenzo Sala ◽  
Salman Ahmad ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Mathematical Model ◽  
Blood Pressure Measurement ◽  
Model Parameters ◽  
Cardiovascular Monitoring ◽  
Cardiovascular Parameters ◽  
Physiological Variables ◽  
Novel Approach ◽  
Cardiovascular Assessment ◽  
Parameter Values

Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure.Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement.Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing.

scholarly journals ESTIMATION OF SEISMIC WAVES PARAMETERS WITH APPLICATION OF A GENETIC ALGORITHM

2018 ◽  
pp. 51-58
Author(s):  
R. R. Sultangaleev ◽  
V. N. Troyan
Keyword(s):  
Genetic Algorithm ◽  
Seismic Waves ◽  
Fitness Function ◽  
Random Number Generator ◽  
Synthetic Data ◽  
Threshold Value ◽  
Velocity Estimation ◽  
Model Parameters ◽  
Basic Step ◽  
The Individual

A Genetic algorithm (GA) is a very important method for the solution of non-linear problems. The basic steps in GA are coding, selection, crossover, mutation and choice. Coding is a way of representing data  in binary notation. The algorithm must determine the fitness of the individual models. This means that  the binary information is decoded into the physical model parameters and the forward problem is solved. The resulting synthetic data is estimated, then compared with the actual observed data using the  specific fitness criteria. The selection of pairs of the individual models for the reproduction is based on  their fitness values. Models with the higher fitness values are more likely to get the selection than models with low  fitness values. A crossover caused the exchange of some information between the paired models thereby  generating new models. The mutation is a random change of binary state. The condition of the procedure of mutation: if a value obtained by a random number generator is less than a certain threshold value, the  mutation procedure is performed. The last basic step in GA is choice. We choose from each pairs a model,  which has the less fitness function. Then we produce the procedures: the crossover, the mutation and the  choice. This procedure is continued until we obtain the optimal model. We have used the GA for the  estimation of the velocity for the gradient layer. The synthetic seismogram was calculated by the finite- difference method. The obtained results showed a high effectiveness of GA for the seismic waves velocity estimation.

Some practical aspects of prestack waveform inversion using a genetic algorithm: An example from the east Texas Woodbine gas sand

Geophysics ◽  
1999 ◽  
Vol 64 (2) ◽  
pp. 326-336 ◽  
Author(s):  
Subhashis Mallick
Keyword(s):  
Genetic Algorithm ◽  
Biological Evolution ◽  
Synthetic Data ◽  
Posteriori Probability ◽  
Inversion Method ◽  
Model Parameters ◽  
East Texas ◽  
Data Set ◽  
A Posteriori Probability ◽  
Prestack Inversion

In this paper, a prestack inversion method using a genetic algorithm (GA) is presented, and issues relating to the implementation of prestack GA inversion in practice are discussed. GA is a Monte‐Carlo type inversion, using a natural analogy to the biological evolution process. When GA is cast into a Bayesian framework, a priori information of the model parameters and the physics of the forward problem are used to compute synthetic data. These synthetic data can then be matched with observations to obtain approximate estimates of the marginal a posteriori probability density (PPD) functions in the model space. Plots of these PPD functions allow an interpreter to choose models which best describe the specific geologic setting and lead to an accurate prediction of seismic lithology. Poststack inversion and prestack GA inversion were applied to a Woodbine gas sand data set from East Texas. A comparison of prestack inversion with poststack inversion demonstrates that prestack inversion shows detailed stratigraphic features of the subsurface which are not visible on the poststack inversion.

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