Nonlinear modeling using support vector machine for heart rate response to exercise

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Computational Intelligence and its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques, 2012, pp. 255 - 270
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© 2012 by Imperial College Press. All rights reserved. In order to accurately regulate cardiovascular response to exercise for an individual exerciser, this study proposed a control oriented modeling approach to depict nonlinear behavior of heart rate response at both the onset and offset of treadmill exercise. With the aim of capturing nonlinear dynamic behaviors, a well designed exercise protocol has been applied for a healthy male subject. Non-invasively measured variables, such as ECG, body movements and oxygen saturation (SpO2), have been reliably monitored and recorded. Based on several sets of experimental data, both steady state gain and time constant of heart rate response are identified. The nonlinear models relating to steady state gain and time constant (vs. walking speed) were built up based on support vector machine regression (SVR). The nonlinear behaviors at both onset and offset of exercise have been well described by using the established SVR models. The model provides the fundamentals for the optimization of exercise efforts by using model-based optimal control approaches, which is the following step of this study.
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