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Medical & Biological Engineering & Computing

, Volume 57, Issue 6, pp 1229–1245 | Cite as

Fusion of heart rate variability and salivary cortisol for stress response identification based on adverse childhood experience

  • Noor Aimie-SallehEmail author
  • M. B. Malarvili
  • Anna C. Whittaker
Original Article

Abstract

Adverse childhood experiences have been suggested to cause changes in physiological processes and can determine the magnitude of the stress response which might have a significant impact on health later in life. To detect the stress response, biomarkers that represent both the Autonomic Nervous System (ANS) and Hypothalamic-Pituitary-Adrenal (HPA) axis are proposed. Among the available biomarkers, Heart Rate Variability (HRV) has been proven as a powerful biomarker that represents ANS. Meanwhile, salivary cortisol has been suggested as a biomarker that reflects the HPA axis. Even though many studies used multiple biomarkers to measure the stress response, the results for each biomarker were analyzed separately. Therefore, the objective of this study is to propose a fusion of ANS and HPA axis biomarkers in order to classify the stress response based on adverse childhood experience. Electrocardiograph, blood pressure (BP), pulse rate (PR), and salivary cortisol (SCort) measures were collected from 23 healthy participants; 11 participants had adverse childhood experience while the remaining 12 acted as the no adversity control group. HRV was then computed from the ECG and the HRV features were extracted. Next, the selected HRV features were combined with the other biomarkers using Euclidean distance (ed) and serial fusion, and the performance of the fused features was compared using Support Vector Machine. From the result, HRV-SCort using Euclidean distance achieved the most satisfactory performance with 80.0% accuracy, 83.3% sensitivity, and 78.3% specificity. Furthermore, the performance of the stress response classification of the fused biomarker, HRV-SCort, outperformed that of the single biomarkers: HRV (61% Accuracy), Cort (59.4% Accuracy), BP (78.3% accuracy), and PR (53.3% accuracy). From this study, it was proven that the fused biomarkers that represent both ANS and HPA (HRV-SCort) able to demonstrate a better classification performance in discriminating the stress response. Furthermore, a new approach for classification of stress response using Euclidean distance and SVM named as ed-SVM was proven to be an effective method for the HRV-SCort in classifying the stress response from PASAT. The robustness of this method is crucial in contributing to the effectiveness of the stress response measures and could further be used as an indicator for future health.

Graphical abstract

Keywords

Heart rate variability Stress Fusion Electrocardiogram Childhood stress Stress detector 

Notes

Funding information

This study was supported by the Research University Funding from Universiti Technologi Malaysia, Malaysia (RUG: Q.J130000.2645.15J59), funding from Ministry of Education, Malaysia (FRGS: R.J130000.7745.4F943) and Prof Whittaker is funded by the University of Birmingham, UK, and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 675003. http://www.birmingham.ac.uk/panini.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Acharya RU, Vidya KS, Ghista DN, Lim WJE, Molinari F, Sankaranarayanan M (2015) Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method. Knowl-Based Syst 81:56–64.  https://doi.org/10.1016/j.knosys.2015.02.005 Google Scholar
  2. 2.
    Acharya UR, Joseph KP, Kannathal N, Lim C, Suri J (2006) Heart rate variability: a review. Med Biol Eng Comput 44:1031–1051.  https://doi.org/10.1007/s11517-006-0119-0 Google Scholar
  3. 3.
    Acharya UR, Sankaranarayanan M, Nayak J, Xiang C, Tamura T (2008) Automatic identification of cardiac health using modeling techniques: a comparative study. Inf Sci 178:4571–4582.  https://doi.org/10.1016/j.ins.2008.08.006 Google Scholar
  4. 4.
    Aimie-Salleh N (2013) Autonomic function assessment tool using time-frequency analysis of heart rate variability. Master thesis, Universiti Teknologi Malaysia, Johor BahruGoogle Scholar
  5. 5.
    Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723.  https://doi.org/10.1109/TAC.1974.1100705 Google Scholar
  6. 6.
    Akay M (1994) Biomedical signal processing. Academic Press, San DiegoGoogle Scholar
  7. 7.
    Akhter N, Dabhade S, Bansod N, Kale K (2016) Feature selection for heart rate variability based biometric recognition using genetic algorithm. In: Berretti S, Thampi SM, Srivastava PR (eds) Intelligent systems technologies and applications. Springer International Publishing, Maharashtra, India, pp 91–101.  https://doi.org/10.1007/978-3-319-23036-8_8 Google Scholar
  8. 8.
    Allen AP, Kennedy PJ, Cryan JF, Dinan TG, Clarke G (2014) Biological and psychological markers of stress in humans: focus on the Trier social stress test. Neurosci Biobehav Rev 38:94–124Google Scholar
  9. 9.
    Anda R, Williamson D, Jones D, Macera C, Eaker E, Glassman A, Marks J (1993) Depressed affect, hopelessness, and the risk of ischemic heart disease in a cohort of U.S. adults. Epidemiology 4:285–294Google Scholar
  10. 10.
    Anda RF, Felitti VJ, Bremner JD, Walker JD, Whitfield C, Perry BD, Dube SR, Giles WH (2006) The enduring effects of abuse and related adverse experiences in childhood. Eur Arch Psychiatry Clin Neurosci 256:174–186.  https://doi.org/10.1007/s00406-005-0624-4 Google Scholar
  11. 11.
    Applegate EJ (1995) The anatomy and physiology learning system, 1st edn. Saunders, PhiladelphiaGoogle Scholar
  12. 12.
    Babatunde O, Leisa A, Leng J, Dean D (2014) A genetic algorithm-based feature selection. Int J Electron Commun Comput Eng 5:899–905Google Scholar
  13. 13.
    Bailon R, Mainardi LT, Laguna P Time-frequency analysis of heart rate variability during stress testing using “a priori” information of respiratory frequency. In: Computers in cardiology, Valencia, Spain, 17–20 September 2006. IEEE, pp 169–172Google Scholar
  14. 14.
    Baldwa VS, Ewing DJ (1977) Heart rate response to Valsalva manoeuvre: reproducibility in normals, and relation to variation in resting heart rate in diabetics. Br Heart J 39:641–644.  https://doi.org/10.1136/hrt.39.6.641 Google Scholar
  15. 15.
    Batten SV, Aslan M, Maciejewski PK, Mazure CM (2004) Childhood maltreatment as a risk factor for adult cardiovascular disease and depression. J Clin Psychiatry 65:249–254Google Scholar
  16. 16.
    Bernardi L, Wdowczyk-Szulc J, Valenti C, Castoldi S, Passino C, Spadacini G, Sleight P (2000) Effects of controlled breathing, mental activity and mental stress with or without verbalization on heart rate variability. J Am Coll Cardiol 35:1462–1469.  https://doi.org/10.1016/S0735-1097(00)00595-7 Google Scholar
  17. 17.
    Bernstein DP, Stein JA, Newcomb MD, Walker E, Pogge D, Ahluvalia T, Stokes J, Handelsman L, Medrano M, Desmond D, Zule W (2003) Development and validation of a brief screening version of the Childhood Trauma Questionnaire. Child Abuse Negl 27:169–190Google Scholar
  18. 18.
    Bibbey A, Ginty AT, Brindle RC, Phillips AC, Carroll D (2016) Blunted cardiac stress reactors exhibit relatively high levels of behavioural impulsivity. Physiol Behav 159:40–44.  https://doi.org/10.1016/j.physbeh.2016.03.011 Google Scholar
  19. 19.
    Birkhofer A, Geissendoerfer J, Alger P, Mueller A, Rentrop M, Strubel T, Leucht S, Förstl H, Bär KJ, Schmidt G (2013) The deceleration capacity—a new measure of heart rate variability evaluated in patients with schizophrenia and antipsychotic treatment. Eur Psychiatry 28:81–86.  https://doi.org/10.1016/j.eurpsy.2011.06.010 Google Scholar
  20. 20.
    Boashash B (2003) Time frequency signal analysis and processing: a comprehensive reference, 1st edn. Elsevier, OxfordGoogle Scholar
  21. 21.
    Borges G, Brusamarello V (2015) Sensor fusion methods for reducing false alarms in heart rate monitoring. J Clin Monit ComputGoogle Scholar
  22. 22.
    Boutana D, Benidir M, Marir F, Barkat B A Comparative study of some time-frequency distributions using Rényi criterion. In: Proceedings of the 13th European signal processing conferences, Antalya, Turkey, 4–8 September 2005. IEEE,Google Scholar
  23. 23.
    Boyce WT, Ellis BJ (2005) Biological sensitivity to context: I. An evolutionary-developmental theory of the origins and functions of stress reactivity. Dev Psychopathol 17:271–301Google Scholar
  24. 24.
    Bricout V, DeChenaud S, Favre-Juvin A (2010) Analyses of heart rate variability in young soccer players: the effects of sport activity. Auton Neurosci 154:112–116.  https://doi.org/10.1016/j.autneu.2009.12.001 Google Scholar
  25. 25.
    Brindle RC, Ginty AT, Conklin SM (2013) Is the association between depression and blunted cardiovascular stress reactions mediated by perceptions of stress? Int J Psychophysiol 90:66–72.  https://doi.org/10.1016/j.ijpsycho.2013.06.003 Google Scholar
  26. 26.
    Carroll D, Phillips AC, Der G (2008) Body mass index, abdominal adiposity, obesity, and cardiovascular reactions to psychological stress in a large community sample. Psychosom Med 70:653–660.  https://doi.org/10.1097/PSY.0b013e31817b9382 Google Scholar
  27. 27.
    Carroll D, Phillips AC, Hunt K, Der G (2007) Symptoms of depression and cardiovascular reactions to acute psychological stress: evidence from a population study. Biol Psychol 75:68–74.  https://doi.org/10.1016/j.biopsycho.2006.12.002 Google Scholar
  28. 28.
    Carroll D, Phillips AC, Ring C, Der G, Hunt K (2005) Life events and hemodynamic stress reactivity in the middle-aged and elderly. Psychophysiology 42:269–276.  https://doi.org/10.1111/j.1469-8986.2005.00282.x Google Scholar
  29. 29.
    Carroll JE, Gruenewald TL, Taylor SE, Janicki-Deverts D, Matthews KA, Seeman TE (2013) Childhood abuse, parental warmth, and adult multisystem biological risk in the coronary artery risk development in young adults study. Proc Natl Acad Sci 110:17149–17153Google Scholar
  30. 30.
    Carvalho JLA, Rocha AF, Junqueira LF, Jr., Neto JS, Santos I, Nascimento FAO A tool for time-frequency analysis of heart rate variability. In: Proceedings of the 25th annual international conference of the IEEE engineering in medicine and biology society, Brasilia University, Brazil, 17–21 September 2003 2003. IEEE, pp 2574–2577Google Scholar
  31. 31.
    Castro MN, Vigo DE, Chu EM, Fahrer RD, de Achával D, Costanzo EY, Leiguarda RC, Nogués M, Cardinali DP, Guinjoan SM (2009) Heart rate variability response to mental arithmetic stress is abnormal in first-degree relatives of individuals with schizophrenia. Schizophr Res 109:134–140.  https://doi.org/10.1016/j.schres.2008.12.026 Google Scholar
  32. 32.
    Castro MN, Vigo DE, Weidema H, Fahrer RD, Chu EM, de Achával D, Nogués M, Leiguarda RC, Cardinali DP, Guinjoan SM (2008) Heart rate variability response to mental arithmetic stress in patients with schizophrenia: autonomic response to stress in schizophrenia. Schizophr Res 99:294–303.  https://doi.org/10.1016/j.schres.2007.08.025 Google Scholar
  33. 33.
    Choi J, Gutierrez-Osuna R Using heart rate monitors to detect mental stress. In: Sixth international workshop on wearable and implantable body sensor networks, USA, 3–5 June 2009. IEEE, pp 219–223Google Scholar
  34. 34.
    Cosetl RC, Lopez JMDB Voice stress detection: a method for stress analysis detecting fluctuations on Lippold microtremor spectrum using FFT. In: 21st International conference on electrical communications and computers Puebla, Mexico, 28 February–2 March 2011. IEEE, pp 184–189.  https://doi.org/10.1109/conielecomp.2011.5749357
  35. 35.
    Cvetkovic D, Ubeyli ED, Cosic I (2008) Wavelet transform feature extraction from human PPG, ECG and EEG signal responses to ELF PEMF exposures: a pilot study. Digital Signal Processing 18:861–874Google Scholar
  36. 36.
    Danese A, Moffitt TE, Harrington H, Milne BJ, Polanczyk G, Pariante CM, Poulton R, Caspi A (2009) Adverse childhood experiences and adult risk factors for age-related disease: depression, inflammation, and clustering of metabolic risk markers. Arch Pediatr Adolesc Med 163:1135–1143.  https://doi.org/10.1001/archpediatrics.2009.214 Google Scholar
  37. 37.
    Deepak A, Deepak A, Nallulwar S, Khode V (2014) Time domain measures of heart rate variability during acute mental stress in type 2 diabetics: a case control study. Natl J Physiol Pharm Pharmacol 4:34–38Google Scholar
  38. 38.
    Dong M, Giles WH, Felitti VJ, Dube SR, Williams JE, Chapman DP, Anda RF (2004) Insights into causal pathways for ischemic heart disease: adverse childhood experiences study. Circulation 110:1761–1766.  https://doi.org/10.1161/01.CIR.0000143074.54995.7F Google Scholar
  39. 39.
    Ellis BJ, Essex MJ, Boyce WT (2005) Biological sensitivity to context: II. Empirical explorations of an evolutionary-developmental theory. Dev Psychopathol 17:303–328Google Scholar
  40. 40.
    Feldman JM, Ebrahim MH, Bar-Kana I (1997) Robust sensor fusion improves heart rate estimation: clinical evaluation. J Clin Monit Comput 13Google Scholar
  41. 41.
    Felitti FVJ, Anda MSRF, Nordenberg D, Williamson DF, Spitz MPHAM, Edwards V, Koss MP, Marks MPHJS (1998) Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: the adverse childhood experiences (ACE) study. Am J Prev Med 14:245–258.  https://doi.org/10.1016/S0749-3797(98)00017-8 Google Scholar
  42. 42.
    Ginty AT, Conklin SM (2011) High perceived stress in relation to life events is associated with blunted cardiac reactivity. Biol Psychol 86:383–385.  https://doi.org/10.1016/j.biopsycho.2011.01.002 Google Scholar
  43. 43.
    Ginty AT, Phillips AC, Der G, Deary IJ, Carroll D (2011) Cognitive ability and simple reaction time predict cardiac reactivity in the West of Scotland Twenty-07 Study. Psychophysiology 48:1022–1027.  https://doi.org/10.1111/j.1469-8986.2010.01164.x Google Scholar
  44. 44.
    Ginty AT, Phillips AC, Higgs S, Heaney JL, Carroll D (2012) Disordered eating behaviour is associated with blunted cortisol and cardiovascular reactions to acute psychological stress. Psychoneuroendocrinology 37:715–724.  https://doi.org/10.1016/j.psyneuen.2011.09.004 Google Scholar
  45. 45.
    Giri D, Acharya UR, Martisb RJ, Sreed SV, Lim T-C, Ahamed VIT, Suri JS (2013) Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform. Knowl-Based Syst 37:274–282Google Scholar
  46. 46.
    Golz M, Sommer D, Chen M, Mandic D, Trutschel U (2007) Feature fusion for the detection of microsleep events. J VLSI Signal Process 49:329–342Google Scholar
  47. 47.
    Gorman JM, Sloan RP (2000) Heart rate variability in depressive and anxiety disorders. Am Heart J 140:S77–S83.  https://doi.org/10.1067/mhj.2000.109981 Google Scholar
  48. 48.
    Goswami DP, Bhattacharya DK, Tibarewala DN (2010) Analysis of heart rate variability in meditation using normalized Shannon entropy. Int J Phys Sci 14:61–67Google Scholar
  49. 49.
    Gronwall D (1977) Paced auditory serial addition task: a measure of recovery from concussion. Percept Mot Skills 44:367–373Google Scholar
  50. 50.
    Guillen P, Vallverdu M, Claria F, Jugo D, Carrasco H, Caminal P (2000) Complexity analysis of heart rate variability applied to chagasic patients and normal subjects. Comput Cardiol 27:469–472Google Scholar
  51. 51.
    Gunn SR (1998) Support vector machines for classification and regression. University of SouthamptonGoogle Scholar
  52. 52.
    Guo XH, Yi G, Batchvarov V, Gallagher MM, Malik M (1999) Effect of moderate physical exercise on noninvasive cardiac autonomic tests in healthy volunteers. Int J Cardiol 69:155–168.  https://doi.org/10.1016/S0167-5273(99)00029-7 Google Scholar
  53. 53.
    Haker E, Egekvist H, Bjerring P (2000) Effect of sensory stimulation (acupuncture) on sympathetic and parasympathetic activities in healthy subjects. J Auton Nerv Syst 79:52–59.  https://doi.org/10.1016/S0165-1838(99)00090-9 Google Scholar
  54. 54.
    Hamer M, O'Donnell K, Lahiri A, Steptoe A (2010) Salivary cortisol responses to mental stress are associated with coronary artery calcification in healthy men and women. Eur Heart J 31:424–429Google Scholar
  55. 55.
    Heaney JLJ, Ginty AT, Carroll D, Phillips AC (2011) Preliminary evidence that exercise dependence is associated with blunted cardiac and cortisol reactions to acute psychological stress. Int J Psychophysiol 79:323–329.  https://doi.org/10.1016/j.ijpsycho.2010.11.010 Google Scholar
  56. 56.
    Hellhammer DH, Wust S, Kudielka BM (2009) Salivary cortisol as a biomarker in stress research. Psychoneuroendocrinology 34:163–171Google Scholar
  57. 57.
    Hellhammer J, Schubert M (2012) The physiological response to Trier social stress test relates to subjective measures of stress during but not before or after the test. Psychoneuroendocrinology 37:119–124.  https://doi.org/10.1016/j.psyneuen.2011.05.012 Google Scholar
  58. 58.
    Hjortskov N, Rissén D, Blangsted A, Fallentin N, Lundberg U, Søgaard K (2004) The effect of mental stress on heart rate variability and blood pressure during computer work. Eur J Appl Physiol 92:84–89.  https://doi.org/10.1007/s00421-004-1055-z Google Scholar
  59. 59.
    Hosseini SA, Khalilzadeh MA, Naghibi-Sistani MB, Homam SM (2015) Emotional stress recognition using a new fusion link between electroencephalogram and peripheral signals Iranian. J Neurol 14:142–151Google Scholar
  60. 60.
    Jacobs JR, Bovasso GB (2000) Early and chronic stress and their relation to breast cancer. Psychol Med 30:669–678Google Scholar
  61. 61.
    Jacobson ML (2003) Acquisition and classification of heart rate variability using time-frequency representation. PhD thesis, Napier University, EdinburghGoogle Scholar
  62. 62.
    Karthikeyan P, Murugappan M, Yaacob S (2013) Multiple physiological signal-based human stress identification using non-linear classifiers. Elektron Elektrotech19:80–85Google Scholar
  63. 63.
    Karthikeyan P, Murugappan M, Yaacob S (2014) Analysis of Stroop color word test-based human stress detection using electrocardiography and heart rate variability signals. Arab J Sci Eng 39:1835–1847.  https://doi.org/10.1007/s13369-013-0786-8 Google Scholar
  64. 64.
    Kendall-Tackett KA (2000) Physiological correlates of childhood abuse: chronic hyperarousal in PTSD, depression, and irritable bowel syndrome. Child Abuse Negl 24:799–810.  https://doi.org/10.1016/S0145-2134(00)00136-8 Google Scholar
  65. 65.
    Khazaee A, Ebrahimzadeh A (2010) Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features. Biomed Signal Process Control 5:252–263Google Scholar
  66. 66.
    Kheder G, Kachouri A, Massoued MB, Samet M (2009) Heart rate variability analysis using threshold of wavelet package coefficients. Int J Comput Sci Eng 1:131–136Google Scholar
  67. 67.
    Kluttig A, Kuss O, Greiser KH (2010) Ignoring lack of association of heart rate variability with cardiovascular disease and risk factors: response to the manuscript “the relationship of autonomic imbalance, heart rate variability cardiovascular disease risk factors” by Julian F. Thayer, Shelby S. Yamamoto, Jos F. Brosschot. Int J Cardiol 145:375–376Google Scholar
  68. 68.
    Kulur AB, Haleagrahara N, Adhikary P, Jeganathan PS (2009) Effect of diaphragmatic breathing on heart rate variability in ischemic heart disease with diabetes. Arq Bras Cardiol 92:423-429, 440-427, 457-463Google Scholar
  69. 69.
    Liew WS, Seera M, Loo CK, Lim E, Kubota N (2016) Classifying stress from heart rate variability using salivary biomarkers as reference. IEEE Trans Neural Netw Learn Syst 27:2035–2046.  https://doi.org/10.1109/tnnls.2015.2468721
  70. 70.
    Little AC, McPherson J, Dennington L, Jones BC (2011) Accuracy in assessment of self-reported stress and a measure of health from static facial information. Personal Individ Differ 51:693–698.  https://doi.org/10.1016/j.paid.2011.06.010 Google Scholar
  71. 71.
    Liu CJ, Wechsler H (2001) A shape and texture based enhanced classifier for face recognition. IEEE Trans Image Process 10:598–608Google Scholar
  72. 72.
    Lovallo WR (2013) Early life adversity reduces stress reactivity and enhances impulsive behavior: implications for health behaviors. Int J Psychophysiol 90:8–16Google Scholar
  73. 73.
    Lovallo WR, Farag NH, Sorocco KH, Cohoon AJ, Vincent AS (2012) Lifetime adversity leads to blunted stress axis reactivity: studies from the Oklahoma Family Health Patterns Project. Biol Psychiatry 71:344–349Google Scholar
  74. 74.
    Luecken LJ, Roubinov DS (2012) Hostile behavior links negative childhood family relationships to heart rate reactivity and recovery in young adulthood. Int J Psychophysiol 84:172–179.  https://doi.org/10.1016/j.ijpsycho.2012.02.003 Google Scholar
  75. 75.
    Malarvili MB (2008) Combining newborn EEG and HRV for automatic seizure detection. PhD Thesis, University of Queensland, QueenslandGoogle Scholar
  76. 76.
    Malarvili MB, Mesbah M (2009) Newborn seizure detection based on heart rate variability. IEEE Trans Biomed Eng 56:2594–2603Google Scholar
  77. 77.
    Malarvili MB, Sucic V, Mesbah M, Boashash B Rényi entropy of quadratic TFD: effects of signal parameters. In: Proceedings of the international symposium on signal processing and its applications, Sharjah, UAE, 12-15 February 2007. IEEE,Google Scholar
  78. 78.
    Mangai UG, Samanta S, Das S, Chowdhury PR (2010) A survey of decision fusion and feature fusion strategies for pattern classification. IETE Tech Rev 27:293–307Google Scholar
  79. 79.
    May O, Arildsen H (2000) Assessing cardiovascular autonomic neuropathy in diabetes mellitus: how many tests to use? J Diabetes Complicat 14:7–12.  https://doi.org/10.1016/S1056-8727(00)00062-3 Google Scholar
  80. 80.
    Mayeux R (2004) Biomarkers: potential uses and limitations. Neurotherapeutics 1:182–188Google Scholar
  81. 81.
    Mehta RK (2014) Impacts of obesity and stress on neuromuscular fatigue development and associated heart rate variability. Int J Obes 39:208–213.  https://doi.org/10.1038/ijo.2014.127 Google Scholar
  82. 82.
    Michels N, Sioen I, Braet C, Huybrechts I, Vanaelst B, Wolters M, Henauw SD (2013) Relation between salivary cortisol as stress biomarker and dietary pattern in children. Psychoneuroendocrinology 38:1512–1520Google Scholar
  83. 83.
    Michels N, Sioen I, Clays E, De Buyzere M, Ahrens W, Huybrechts I, Vanaelst B, De Henauw S (2013) Children’s heart rate variability as stress indicator: association with reported stress and cortisol. Biol Psychol 94:433–440.  https://doi.org/10.1016/j.biopsycho.2013.08.005 Google Scholar
  84. 84.
    Minakuchi E, Ohnishi E, Ohnishi J, Sakamoto S, Hori M, Motomura M, Hoshino J, Murakami K, Kawaguchi T (2013) Evaluation of mental stress by physiological indices derived from finger plethysmography. J Physiol Anthropol 32Google Scholar
  85. 85.
    Muaremi A, Arnrich B, Tröster G (2013) Towards measuring stress with smartphones and wearable devices during workday and sleep. BioNanoSci 3:172–183.  https://doi.org/10.1007/s12668-013-0089-2 Google Scholar
  86. 86.
    Nater UM, Rohleder N, Gaab J, Berger S, Jud A, Kirschbaum C, Ehlert U (2005) Human salivary alpha-amylase reactivity in a psychosocial stress paradigm. Int J Psychophysiol 55:333–342.  https://doi.org/10.1016/j.ijpsycho.2004.09.009 Google Scholar
  87. 87.
    Neto OP, Oliveira Pinheiro A, Pereira VL Jr, Pereira R, Baltatu OC, Campos LA (2016) Morlet wavelet transforms of heart rate variability for autonomic nervous system activity. Appl Comput Harmon Anal 40:200–206.  https://doi.org/10.1016/j.acha.2015.07.002 Google Scholar
  88. 88.
    Nguyen TA, Zeng Y (2013) A physiological study of relationship between designer’s mental effort and mental stress during conceptual design. Comput Aided Des 54:3–18.  https://doi.org/10.1016/j.cad.2013.10.002 Google Scholar
  89. 89.
    Nomura S, Mizuno T, Nozawa A, Asana H, Ide H Salivary cortisol as a new biomarker for a mild mental workload. In: International conference in biometrics and Kansei engineering, Japan, 25–28 June 2009. IEEE, pp 127–131Google Scholar
  90. 90.
    Ockenburg SLV, Tak LM, Bakker SJL, Gans ROB, Jonge PD, Rosmalen JGM (2014) Effects of adverse life events on heart rate variability, cortisol, and C-reactive protein. Acta Psychiatr Scand 131:40–50Google Scholar
  91. 91.
    Ollander S (2015) Wearable sensor data fusion for human stress estimation. Master thesis, Linköping University, LinköpingGoogle Scholar
  92. 92.
    Oluleye B, Leisa A, Leng J, Dean D (2014) Zernike moments and genetic algorithm: tutorial and application. Brit J Math Comput Sci 4:2217–2236Google Scholar
  93. 93.
    Pan J, Tompkins JW (1985) A real-time QRS detection algorithm. IEEE Trans Biomed Eng 32:230–236Google Scholar
  94. 94.
    Peressutti C, Martín-González JM, García-Manso JM, Mesa D (2010) Heart rate dynamics in different levels of Zen meditation. Int J Cardiol 145:142–146.  https://doi.org/10.1016/j.ijcard.2009.06.058 Google Scholar
  95. 95.
    Phillips AC (2011) Blunted cardiovascular reactivity relates to depression, obesity, and self-reported health. Biol Psychol 86:106–113.  https://doi.org/10.1016/j.biopsycho.2010.03.016 Google Scholar
  96. 96.
    Phillips AC, Carroll D, Ring C, Sweeting H, West P (2005) Life events and acute cardiovascular reactions to mental stress: a cohort study. Psychosom Med 67:384–392Google Scholar
  97. 97.
    Phillips AC, Der G, Hunt K, Carroll D (2009) Haemodynamic reactions to acute psychological stress and smoking status in a large community sample. Int J Psychophysiol 73:273–278.  https://doi.org/10.1016/j.ijpsycho.2009.04.005 Google Scholar
  98. 98.
    Phillips AC, Ginty AT, Hughes BM (2013) The other side of the coin: blunted cardiovascular and cortisol reactivity are associated with negative health outcomes. Int J Psychophysiol 90:1–7.  https://doi.org/10.1016/j.ijpsycho.2013.02.002 Google Scholar
  99. 99.
    Phillips AC, Hunt K, Der G, Carroll D (2010) Blunted cardiac reactions to acute psychological stress predict symptoms of depression five years later: evidence from a large community study. Psychophysiology 48:142–148.  https://doi.org/10.1111/j.1469-8986.2010.01045.x Google Scholar
  100. 100.
    Phillips AC, Roseboom TJ, Carroll D, de Rooij SR (2012) Cardiovascular and cortisol reactions to acute psychological stress and adiposity: cross-sectional and prospective associations in the Dutch Famine Birth Cohort Study. Psychosom Med 74:699–710.  https://doi.org/10.1097/PSY.0b013e31825e3b91 Google Scholar
  101. 101.
    Phongsuphap S, Pongsupap Y, Chandanamattha P, Lursinsap C (2008) Changes in heart rate variability during concentration meditation. Int J Cardiol 130:481–484.  https://doi.org/10.1016/j.ijcard.2007.06.103 Google Scholar
  102. 102.
    Pincus AM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci 88:2297–2301Google Scholar
  103. 103.
    Pincus SM, Gladstone IM, Richard AE (1991) A regularity statistic for medical data analysis. J Clin Monit Comput 7:335–345Google Scholar
  104. 104.
    Ping S, Sijung H, Yisheng Z (2008) A preliminary attempt to understand compatibility of photoplethysmographic pulse rate variability with electrocardiogramic heart rate variability. J Med Biol Eng 28:173–180Google Scholar
  105. 105.
    Pole NT, Otte C (2007) Associations between childhood trauma and emotion-modulated psychophysiological responses to startling sounds: a study of police cadets. J Abnorm Psychol 116:352–361Google Scholar
  106. 106.
    Raol JR (2009) Multi-sensor data fusion with MATLAB, 1st edn. CRC Press, New YorkGoogle Scholar
  107. 107.
    Rice VH (2012) Theories of stress and its relationship to health. In: Rice VH (ed) Handbook of stress, coping, and health: implications for nursing research, theory, and practice, 2nd edn. SAGE Publications, Inc, Detroit, pp 23–42Google Scholar
  108. 108.
    Richman JS, Mooran JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol 278:H2039–H2049Google Scholar
  109. 109.
    Riera A, Soria-Frisch A, Albajes-Eizagirre A, Cipresso P, Grau C, Dunne S, Ruffini G (2012) Electro-physiological data fusion for stress detection. Annual Review of Cybertherapy and Telemedicine 228–232Google Scholar
  110. 110.
    Ring C, Harrison LK, Winzer A, Carroll D, Drayson M, Kendall M (2000) Secretory immunoglobulin A and cardiovascular reactions to mental arithmetic, cold pressor, and exercise: effects of alpha-adrenergic blockade. Psychophysiology 37:634–643.  https://doi.org/10.1111/1469-8986.3750634 Google Scholar
  111. 111.
    Schneiderman N, Ironson G, Siegel SD (2005) Stress and health: psychological, behavioral, and biological determinant. Annu Rev Clin Psychol 1:607–628Google Scholar
  112. 112.
    Seong HM, Lee JS, Shin TM, Kim WS, Yoon YR, Yoon YR The analysis of mental stress using time-frequency distribution of heart rate variability signal. In: Proceedings of the 26th annual international conference of the IEEE engineering in medicine and biology society, San Francisco, California, 1–5 September 2004. IEEE, pp 283–295Google Scholar
  113. 113.
    Shannon CE (1948) A mathematical theory of communication. Bell Labs Tech J 27:379–423Google Scholar
  114. 114.
    Sharma N, Gedeon T (2012) Objective measures, sensors and computational techniques for stress recognition and classification: a survey. Comput Methods Prog Biomed 108:1287–1302Google Scholar
  115. 115.
    Sierra AdS, Ávila CS, Pozo GBd, Casanova JG Stress detection by means of stress physiological template. In: Third World congress on nature and biologically inspired computing, Salamanca, Spain, 19–21 October 2011. IEEE, pp 131–136.  https://doi.org/10.1109/NaBIC.2011.6089448
  116. 116.
    Soman K, Sathiya A, Suganti N Classification of stress of automobile drivers using radial basis function kernel support vector machine In: International conference on information communication and embedded systems, Chennai, Tamil Nadu, India, 27–28 February 2014. IEEE,Google Scholar
  117. 117.
    Stalder T, Evans P, Hucklebridge F, Clow A (2011) Associations between the cortisol awakening response and heart rate variability. Psychoneuroendocrinology 36:454–462.  https://doi.org/10.1016/j.psyneuen.2010.07.020 Google Scholar
  118. 118.
    Sun F-T, Kuo C, Cheng H-T, Buthpitiya S, Collins P, Griss M (2012) Activity-aware mental stress detection using physiological sensors. In: Gris M, Yang G (eds) Mobile computing, applications, and services, vol 76. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, pp 211–230.  https://doi.org/10.1007/978-3-642-29336-8_12
  119. 119.
    Takai N, Yamaguchi M, Aragaki T, Eto K, Uchihashi K, Nishikawa Y (2004) Effect of psychological stress on the salivary cortisol and amylase levels in healthy young adults. Arch Oral Biol 49:963–968.  https://doi.org/10.1016/j.archoralbio.2004.06.007 Google Scholar
  120. 120.
    Takalo RH, Ihalainen HH (2006) Tutorial on univariate autoregressive spectral analysis export. J Clin Monit Comput 20:379–379.  https://doi.org/10.1234/12345678 Google Scholar
  121. 121.
    Task Force (1996) Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Task force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation 93:1043–1065.  https://doi.org/10.1161/01.CIR.93.5.1043 Google Scholar
  122. 122.
    Tealman J, Huffel SV, Spaepen A Wavelet independent component analysis to remove electrocardiography contamination in surface electromyography. In: 29th Annual international conference of the IEEE engineering in medicine and biology society, Lyon, France, 22–26 August 2007. IEEE, pp 682–685Google Scholar
  123. 123.
    Thayer JF, Åhs F, Fredrikson M, Sollers Iii JJ, Wager TD (2012) A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health. Neurosci Biobehav Rev 36:747–756.  https://doi.org/10.1016/j.neubiorev.2011.11.009 Google Scholar
  124. 124.
    Utsey SO, Abrams JA, Hess DW, McKinley W (2014) Heart rate variability as a correlate of trauma symptom expression, psychological well-being, and emotion regulation in African Americans with traumatic spinal cord injury. J Black Psychol 41:1–12.  https://doi.org/10.1177/0095798414532186 Google Scholar
  125. 125.
    Verkuil B, Brosschot JF, Thayer JF (2014) Cardiac reactivity to and recovery from acute stress: temporal associations with implicit anxiety. Int J Psychophysiol 92:85–91.  https://doi.org/10.1016/j.ijpsycho.2014.03.002 Google Scholar
  126. 126.
    Vuksanovic V, Gal V (2007) Heart rate variability in mental stress aloud. Med Eng Phys 29:344–349.  https://doi.org/10.1016/j.medengphy.2006.05.011 Google Scholar
  127. 127.
    Wachowiak MP, Hay DC, Johnson MJ (2016) Assessing heart rate variability through wavelet-based statistical measures. Comput Biol Med 77:222–230.  https://doi.org/10.1016/j.compbiomed.2016.07.008 Google Scholar
  128. 128.
    Widjaja D, Orini M, Vlemincx E, Van Huffel S (2013) Cardiorespiratory dynamic response to mental stress: a multivariate time-frequency analysis. Comput Math Methods Med 2013:1–12Google Scholar
  129. 129.
    Wijsman J, Grundlehner B, Penders J, Hermens H (2013) Trapezius muscle EMG as predictor of mental stress. ACM Trans Embed Comput Syst 12:99:91-99:20Google Scholar
  130. 130.
    Willemsen G, Ring C, Carroll D, Evans P, Clow A, Hucklebridge F (1998) Secretory immunoglobulin A and cardiovascular reactions to mental arithmetic and cold pressor. Psychophysiology 35:252–259.  https://doi.org/10.1111/1469-8986.3530252 Google Scholar
  131. 131.
    Winzeler K, Voellmin A, Hug E, Kirmse U, Helmig S, Princip M, Cajochen C, Bader K, Wilhelm FH (2017) Adverse childhood experiences and autonomic regulation in response to acute stress: the role of the sympathetic and parasympathetic nervous systems. Anxiety Stress Coping 30:145–154.  https://doi.org/10.1080/10615806.2016.1238076 Google Scholar
  132. 132.
    Yang J, Yang J-y, Zhang D, Lu J-f (2003) Feature fusion: parallel strategy vs. serial strategy. Pattern Recogn 36:1369–1381Google Scholar
  133. 133.
    Yu X, Zhang J (2012) Estimating the cortex and autonomic nervous activity during a mental arithmetic task. Biomedical Signal Processing and Control 7:303–308.  https://doi.org/10.1016/j.bspc.2011.06.001 Google Scholar
  134. 134.
    Zhai J, Barreto A Stress detection in computer users based on digital signal processing of noninvasive physiological variables. In: Annual international conference of the IEEE engineering in medicine and biology society, New York, 30 August 3 September 2006. IEEE, pp 1355–1358Google Scholar
  135. 135.
    Zhang J (2007) Effect of age and sex on heart rate variability in healthy subjects. J Manip Physiol Ther 30:374–379.  https://doi.org/10.1016/j.jmpt.2007.04.001 Google Scholar

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© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.School of Biomedical Engineering and Health Sciences, Faculty of EngineeringUniversiti Teknologi MalaysiaJohor BahruMalaysia
  2. 2.School of Sport, Exercise, and Rehabilitation SciencesUniversity of BirminghamBirminghamUK

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