Seongryul Park
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Seongryul Park has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Ho Bin Hwang
Department of Biomedical Engineering, Hanyang University, Seoul 04763, Republic of Korea
1 shared publication - Hoon-Ki Park
Department of Family Medicine, Hanyang University College of Medicine, Seoul, Korea
1 shared publication - In Young Kim
Hanyang University
1 shared publication - Jongshill Lee
Department of Biomedical Engineering, Hanyang University, Seoul 04763, Republic of Korea
1 shared publication - Jung Hee Ha
Graduate School of Counseling Psychology, Hanyang University, Seoul, South Korea
1 shared publication - Sanghag Kim
Department of Sociology, Hanyang University, Seoul, South Korea
1 shared publication
Latest Publications
- Open Access
- 27 June 2022
- Published by MDPI AG in Biosensors
- Vol. 12 (7) , 465
Mental Stress Assessment Using Ultra Short Term HRV Analysis Based on Non-Linear Method
Mental stress is on the rise as one of the major health problems in modern society. It is important to detect and manage mental stress to prevent various diseases caused by stress and to maintain a healthy life. The purpose of this paper is to present new heart rate variability (HRV) features based on empirical mode decomposition and to detect acute mental stress through short-term HRV (5 min) and ultra-short-term HRV (under 5 min) analysis. HRV signals were acquired from 74 young police officers using acute stressors, including the Trier Social Stress Test and horror movie viewing, and a total of 26 features, including the proposed IMF energy features and general HRV features, were extracted. A support vector machine (SVM) classification model is used to classify the stress and non-stress states through leave-one-subject-out cross-validation. The classification accuracies of short-term HRV and ultra-short-term HRV analysis are 86.5% and 90.5%, respectively. In the results of ultra-short-term HRV analysis using various time lengths, we suggest the optimal duration to detect mental stress, which can be applied to wearable devices or healthcare systems.
- 27 June 2022
- Published by MDPI AG in Biosensors
- Vol. 12 (7) , 465
Open AccessMental Stress Assessment Using Ultra Short Term HRV Analysis Based on Non-Linear Method
Mental stress is on the rise as one of the major health problems in modern society. It is important to detect and manage mental stress to prevent various diseases caused by stress and to maintain a healthy life. The purpose of this paper is to present new heart rate variability (HRV) features based on empirical mode decomposition and to detect acute mental stress through short-term HRV (5 min) and ultra-short-term HRV (under 5 min) analysis. HRV signals were acquired from 74 young police officers using acute stressors, including the Trier Social Stress Test and horror movie viewing, and a total of 26 features, including the proposed IMF energy features and general HRV features, were extracted. A support vector machine (SVM) classification model is used to classify the stress and non-stress states through leave-one-subject-out cross-validation. The classification accuracies of short-term HRV and ultra-short-term HRV analysis are 86.5% and 90.5%, respectively. In the results of ultra-short-term HRV analysis using various time lengths, we suggest the optimal duration to detect mental stress, which can be applied to wearable devices or healthcare systems.