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 or...

Full description

Saved in:
Bibliographic Details
Published in2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) pp. 269 - 272
Main Authors Giannakakis, Giorgos, Marias, Kostas, Tsiknakis, Manolis
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2019
Subjects
ECG
Electrocardiography
Feature extraction
heart rate
Heart rate variability
HRV
pairwise transformation
Reliability
Stress
stress classification
Support vector machines
Task analysis
Online AccessGet full text

Cover

More Information
Summary: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.
DOI:10.1109/ACIIW.2019.8925142