On Modeling the Cardiovascular System and Predicting the Human Heart Rate under Strain

Abstract

With the increasing average age of the population in many developed countries, afflictions like cardiovascular diseases have also increased. Exercising has a proven therapeutic effect on the cardiovascular system and can counteract this development. To avoid overstrain, determining an optimal training dose is crucial. In previous research, heart rate has been shown to be a good measure for cardiovascular behavior. Hence, prediction of the heart rate from work load information is an essential part in models used for training control. Most heart-ratebased models are described in the context of specific scenarios, and have been evaluated on unique datasets only. In this paper, we conduct a joint evaluation of existing approaches to model the cardiovascular system under a certain strain, and compare their predictive performance. For this purpose, we investigated some analytical models as well as some machine learning approaches in two scenarios: prediction over a certain time horizon into the future, and estimation of the relation between work load and heart rate over a whole training session. 1 This figure is extrapolated from the cost incurred in Germany by burnout , cardiovascular diseases, and obesity only, which in 2010 totaled to approx. 103 bn EUR. It does not include other major cost driver such as athrosis or dementia. In Europe the cost incurred by cardiovascular diseases only amounted to 195 bn EUR (Nichols et al., 2012) in 2012.

Measurement, Prediction, and Control of Individual Heart Rate Responses to Exercise—Basics and Options for Wearable Devices

“…Adjustment of short term prediction models for the usage of session prediction is mathematically possible, but can lead to a lack of accuracy as shown in Ludwig et al ( 2015 ) and Hoffmann and Wiemeyer ( 2017a ). If a short term prediction model makes use of previous HR values, respective previously computed HR values could be used in the corresponding session prediction model.…”

“…Particularly interpretability of a model's parameters is favorable in HR prediction: to model not artifacts but real factors influencing the HR significantly improves the accuracy of prediction. Ludwig et al ( 2015 ) gives a comparison of different types of phenomenological models and presents their accuracy in approximation and prediction of different time horizons of HR. The results illustrate that good accuracy in approximation or prediction of few seconds does not transfer to prediction accuracy in session prediction.…”

“…Hence, no conclusions were drawn for correlations between HR and other cardiovascular factors. Ludwig et al ( 2015 ) showed that support vector regression can also be used to simulate and predict HR dynamics based upon earlier HR measurements. Esmaeili and Ibeas ( 2016 ) applied a particle swarm optimization method for the SVR model proposed by Wang et al ( 2008 ) and claimed to reach better model parameters compared to other studies.…”

Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning

“…In the medical field, this study builds upon a fast-expanding literature that applies machine learning tools in health and healthcare forecasting. For example, the risk of the onset of a disease, whether it be cardiovascular [ 26 , 27 ] or not [ 28 ]; hospital discharge volume [ 29 ]; arrhythmia prevention [ 30 ], response to training in healthy individuals [ 31 ] or in individuals with pathologies [ 32 ]; identification of the existence of heart diseases [ 33 ]; or evaluation of the risk of mortality in subjects who had a heart attack during the previous year [ 34 ].…”

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“…Informative data on changes in HRV were obtained in the analysis of recovery processes in the body after performing physical activity of varying intensity [9,10,11]. A number of studies in which the intensity of physical activity was selected based on the changes in the HRV indices allowed to prove the effectiveness of this approach from the point of view of the development of the level of training [12,13]. Other studies [14] have shown that HRV changes in accordance with maximum absorption of oxygen and lactic acid levels.…”