Binary classification of HRV signals
Abstract: Heart rate variability, commonly abbreviated as HRV, displays the variance between consecutive heartbeats. This variance occurs naturally but can change due to stress and problems with the cardiac system. HRV is therefore widely used for medical research. The goal of this thesis is to correctly classify two HRV signals where one is obtained at a resting state, the warm signal, while the cold signal is obtained during a simulation of stress. The use of spectral estimation methods leads to the analysis of the high frequency range (0.12 - 0.4 Hz) as well as the analysis of a more narrow frequency band around the respiratory maximum. The analysis of those frequency ranges is done by using linear models as well as studying how the energy of the cold and the warm signal is distributed. All approaches lead to binary classification with more than 50% accuracy. However, the best results are obtained when analyzing the frequency band around the respiratory maximum located at 0.2 Hz or higher. When using a linear model for changes in energy over time, dividing the data into four sets leads to 93.4% correct classification. When analyzing the energy that is present in the first 90 s of each signal, 96.23% correct classification is obtained.
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