A stress recognition system using HRV parameters and machine learning techniques

In this study, we investigate reliable heart rate variability (HRV) parameters in order to recognize stress. An experiment protocol was established including different stressors which correspond to a range of everyday life conditions. A personalized baseline was formulated for each participant in order to eliminate inter-subject variability and to normalize data providing a common reference for the whole dataset. The extracted HRV features were transformed accordingly using the pairwise transformation in order to take into account the personalized baseline of each phase in constructing the stress model. The most robust features were selected using the minimum Redundancy Maximum Relevance (mRMR) selection algorithm. The selected features fed machine learning systems achieving a classification accuracy of 84.4% using 10-fold cross-validation.