Analysis of Stroop Color Word Test-Based Human Stress Detection using Electrocardiography and Heart Rate Variability Signals

• Published in: Arabian Journal for Science and Engineering Vol. 39; no. 3; pp. 1835 - 1847
• Main Authors: Karthikeyan, P., Murugappan, M., Yaacob, S.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2014
• Subjects: Classification; Electrocardiography; Engineering; Feature extraction; Frequency bands; Frequency domain analysis; Heart rate; Humanities and Social Sciences; multidisciplinary; Research Article - Computer Engineering and Computer Science; Science; Stresses; Wavelet; Heart rate variability (HRV); Discrete wavelet transform (DWT); K nearest neighbor (KNN); Human stress; Stroop colour word test; Electrocardiogram (ECG)
• DOI: 10.1007/s13369-013-0786-8
• ISSN: 1319-8025

A stress assessment based on the electrocardiography (ECG) and heart rate variability (HRV) signals is described in this paper. The Stroop color word test (stressor) was used to induce stress, and the ECG signal was acquired throughout the experiment to identify the variations that are induced by this stressor. A total of 10 female subjects (aged 20–25 years) participated in this study. A time and frequency domain analysis of the HRV and ECG signals was done to extract the stress-related features. A total of five frequency bands and ratios of the HRV signal were used to analyze the new and existing statistical features. The results indicate that significant changes between the normal and stressed states are more evident with a classification accuracy of 79.17 %. Alternatively, the low frequency range (0.04–0.5 Hz) of the ECG signal (0–100 Hz) was used to identify the effect of stress instead of the usual frequency domain analysis of the HRV signal (0.04–0.5 Hz). To extract the stress-related features of the ECG signal, a discrete wavelet transform based feature extraction was performed using the “ db4 ” and “ coif5 ” wavelet functions. A set of eight statistical features was extracted from the two different frequency bands and the three frequency band ratios. All of the extracted features were classified into two states (stress and normal) using the simple non-linear K-nearest neighbor classifier. The experimental results gave the maximum average accuracy of 94.58 and 94.22 % with the “ db4 ” and “ coif5 ” wavelet functions, respectively. Remarkably, the classification results obtained with the features of the ECG and HRV signals were completely independent of the post-task questionnaire. The outcome of this work was helpful to develop the multiple physiological signal based stress system using optimal features in these two signals.