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


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


Heart rate variability Stress Fusion Electrocardiogram Childhood stress Stress detector 


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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© 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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