Classifying Stress From Heart Rate Variability Using Salivary Biomarkers as Reference

An accurate and noninvasive stress assessment from human physiology is a strenuous task. In this paper, a pattern recognition system to learn complex correlates between heart rate variability (HRV) features and salivary stress biomarkers is proposed. Using the Trier social stress test, heart rate and salivary measurements were obtained from volunteers under varying levels of stress induction. Measurements of salivary alpha-amylase and cortisol were used as objective measures of stress, and were correlated with the HRV features using fuzzy ARTMAP (FAM). In improving the predictive ability of the ARTMAPs, techniques, such as genetic algorithms for parameter optimization and voting ensembles, were employed. The ensemble of FAMs can be used for predicting stress responses of salivary alpha-amylase or cortisol using heart rate measurements as the input. Using alpha-amylase as the stress indicator, the ensemble was able to classify stress from heart rate features with 75% accuracy, and 80% accuracy when cortisol was used.

[1]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[2]  Lizawati Salahuddin,et al.  Detection of Acute Stress by Heart Rate Variability Using a Prototype Mobile ECG Sensor , 2006 .

[3]  Gail A. Carpenter,et al.  Biased ART: A neural architecture that shifts attention toward previously disregarded features following an incorrect prediction , 2010, Neural Networks.

[4]  Zhiwei Zhu,et al.  A Real-Time Human Stress Monitoring System Using Dynamic Bayesian Network , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[5]  Steven J. Simske,et al.  Performance analysis of pattern classifier combination by plurality voting , 2003, Pattern Recognit. Lett..

[6]  James R. Williamson,et al.  Gaussian ARTMAP: A Neural Network for Fast Incremental Learning of Noisy Multidimensional Maps , 1996, Neural Networks.

[7]  Masoud Yaghini,et al.  GOFAM: a hybrid neural network classifier combining fuzzy ARTMAP and genetic algorithm , 2011, Artificial Intelligence Review.

[8]  Ramaswamy Palaniappan,et al.  Using genetic algorithm to select the presentation order of training patterns that improves simplified fuzzy ARTMAP classification performance , 2009, Appl. Soft Comput..

[9]  Mansooreh Mollaghasemi,et al.  An Adaptive Multiobjective Approach to Evolving ART Architectures , 2010, IEEE Transactions on Neural Networks.

[10]  J. Kiecolt-Glaser,et al.  Psychoneuroimmunology: psychological influences on immune function and health. , 2002, Journal of consulting and clinical psychology.

[11]  Chu Kiong Loo,et al.  Accurate and reliable diagnosis and classification using probabilistic ensemble simplified fuzzy ARTMAP , 2005, IEEE Transactions on Knowledge and Data Engineering.

[12]  Witold Pedrycz,et al.  A new selective neural network ensemble with negative correlation , 2012, Applied Intelligence.

[13]  Luiz Eduardo Soares de Oliveira,et al.  Multi-objective Genetic Algorithms to Create Ensemble of Classifiers , 2005, EMO.

[14]  Erik D. Goodman,et al.  The hierarchical fair competition (HFC) model for parallel evolutionary algorithms , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[15]  Euntai Kim,et al.  An efficient genetic selection of the presentation order in simplified fuzzy ARTMAP patterns , 2014, Appl. Soft Comput..

[16]  Xin Yao,et al.  Evolutionary ensembles with negative correlation learning , 2000, IEEE Trans. Evol. Comput..

[17]  Hongbing Ji,et al.  TPPFAM: Use of threshold and posterior probability for category reduction in fuzzy ARTMAP , 2014, Neurocomputing.

[18]  Gholam Ali Montazer,et al.  Font-based persian character recognition using Simplified Fuzzy ARTMAP neural network improved by fuzzy sets and Particle Swarm Optimization , 2009, 2009 IEEE Congress on Evolutionary Computation.

[19]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.

[20]  Wei Tang,et al.  Ensembling neural networks: Many could be better than all , 2002, Artif. Intell..

[21]  Henrik Boström,et al.  Ensemble member selection using multi-objective optimization , 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.

[22]  C. Kirschbaum,et al.  The 'Trier Social Stress Test'--a tool for investigating psychobiological stress responses in a laboratory setting. , 1993, Neuropsychobiology.

[23]  Ricardo Gutierrez-Osuna,et al.  Using Heart Rate Monitors to Detect Mental Stress , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[24]  Issam Dagher,et al.  An ordering algorithm for pattern presentation in fuzzy ARTMAP that tends to improve generalization performance , 1999, IEEE Trans. Neural Networks.

[25]  Chu Kiong Loo,et al.  Genetic-Optimized Classifier Ensemble for Cortisol Salivary Measurement Mapping to Electrocardiogram Features for Stress Evaluation , 2012, PRICAI.

[26]  Leander van der Meij,et al.  Salivary alpha-amylase response to acute psychosocial stress: The impact of age , 2011, Biological Psychology.

[27]  Zili Zhang,et al.  An Ensemble of Classifiers with Genetic Algorithm Based Feature Selection , 2008, IEEE Intell. Informatics Bull..

[28]  Nicolas Rohleder,et al.  Human salivary alpha-amylase reactivity in a psychosocial stress paradigm. , 2005, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[29]  Ulrike Ehlert,et al.  The level of physical activity affects adrenal and cardiovascular reactivity to psychosocial stress , 2009, Psychoneuroendocrinology.

[30]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[31]  Luiz Eduardo Soares de Oliveira,et al.  Particle Swarm Optimization of Fuzzy ARTMAP Parameters , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[32]  Sung-Kwun Oh,et al.  Structural and parametric design of fuzzy inference systems using hierarchical fair competition-based parallel genetic algorithms and information granulation , 2008, Int. J. Approx. Reason..

[33]  Nitesh V. Chawla,et al.  Evolutionary Ensemble Creation and Thinning , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[34]  Bogdan Gabrys,et al.  Analysis of the Correlation Between Majority Voting Error and the Diversity Measures in Multiple Classifier Systems , 2001 .

[35]  Ki H. Chon,et al.  Nonlinear analysis of the separate contributions of autonomic nervous systems to heart rate variability using principal dynamic modes , 2004, IEEE Transactions on Biomedical Engineering.

[36]  Edith Filaire,et al.  Effect of lecturing to 200 students on heart rate variability and alpha-amylase activity , 2010, European Journal of Applied Physiology.

[37]  Mou Ling Dennis Wong,et al.  A genetic algorithm for optimizing gravity die casting's heat transfer coefficients , 2011, Expert Syst. Appl..