Abstract
According to the World Health Organization, stress is a considerable problem that affects both the mental well-being and physical health of people, so stress detection becomes an important task. Various stress detection methods based on the human brain and human behavior exist, but none of them uses both brain signals and heart signals together to detect stress. In this paper, a novel approach to detect stress using EEG and ECG signals is proposed. The proposed Stress Recognition by Neuroanalysis (Se.Re.Ne.) method is validated for k-nearest neighbors (KNN) and decision tree (DT) using the correlation method. Results evaluated using Se.Re.Ne. with KNN detect stress with a precision of 0.87, recall of 0.71, and f1-score of 0.78 with total accuracy of 68%, whereas Se.Re.Ne. with DT detects stress with a precision of 0.85, recall of 0.84, and f1-score of 0.84 with a total accuracy of 75%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Abbreviations
- AUC:
-
Area Under the Curve
- CNN:
-
Convolutional Neural Network
- DT:
-
Decision Tree
- EEG:
-
Electroencephalography
- ECG:
-
Electrocardiography
- KNN:
-
K Nearest Neighbours
- MFI:
-
Multidimensional Feature Image
- MIST:
-
Montreal Imaging Stress Task
- ML:
-
Machine Learning
- NN:
-
Neural Network
- PSD:
-
Power Spectral Density
- PAD:
-
Pleasure, Arousal and Dominance
- PFC:
-
Prefrontal Cortex
- ROC:
-
Receiver Operating Characteristic
- SVM:
-
Support Vector Machines
- TKEO:
-
Teager–Kaiser energy operator
References
Goel S, Kaur G, Tomar P (2017) A novel technique for stress recognition using ECG signal pattern. Current Pediatr Res 21(4)
Russell JA (1980) A circumplex model of affect. J Pers Soc Psychol 39(6):1161
Lahane PU, Thirugnanam M (2019) Human emotion detection and stress analysis using EEG signal
Li Y, Huang J, Zhou H, Zhong N (2017) Human emotion recognition with electroencephalographic multidimensional features by hybrid deep neural networks. Appl Sci 7(10):1060
Kalas MS, Momin BF (2018) Modelling EEG dataset for stress state recognition using decision tree approach, pp 82–88
Patel MJ, Khalaf A, Aizenstein HJ (2016) Studying depression using imaging and machine learning methods. NeuroImage Clin 10:115–123
Subhani AR, Mumtaz W, Saad MNBM, Kamel N, Malik AS (2017) Machine learning framework for the detection of mental stress at multiple levels. IEEE Access 5:13545–13556
Al-Shargie F, Tang TB, Kiguchi M (2017) Assessment of mental stress effects on prefrontal cortical activities using canonical correlation analysis: an fNIRS-EEG study. Biomed Opt Express 8(5):2583–2598
Liu Y, Sourina O (2013, Oct) EEG databases for emotion recognition. In: 2013 international conference on cyberworlds. IEEE, pp 302–309
Katsigiannis S, Ramzan N (2017) DREAMER: A database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE J Biomed Health Info In press. https://doi.org/10.1109/JBHI.2017.2688239
Reuderink B, Mühl C, Poel M (2013) Valence, arousal and dominance in the EEG during game play. Int J Autonom Adapt Commun Syst 6(1):45–62
So WK, Wong SW, Mak JN, Chan RH (2017) An evaluation of mental workload with frontal EEG. PloS One 12(4)
Li M, Xu H, Liu X, Lu S (2018) Emotion recognition from multichannel EEG signals using K-nearest neighbor classification. Technol Health Care 26(S1):509–519
Faraz S, Ali SSA, Adil SH (2018, Dec) Machine learning and stress assessment: a review. In: 2018 3rd international conference on emerging trends in engineering, sciences and technology (ICEEST). IEEE, pp 1–4
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Virmani, D., Minocha, A., Goyal, L., Malhotra, M., Gupta, M. (2021). Se.Re.Ne.: Stress Detection Using EEG and ECG. In: Abraham, A., Castillo, O., Virmani, D. (eds) Proceedings of 3rd International Conference on Computing Informatics and Networks. Lecture Notes in Networks and Systems, vol 167. Springer, Singapore. https://doi.org/10.1007/978-981-15-9712-1_16
Download citation
DOI: https://doi.org/10.1007/978-981-15-9712-1_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-9711-4
Online ISBN: 978-981-15-9712-1
eBook Packages: EngineeringEngineering (R0)