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

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

Stress response index for adverse childhood experience based on fusion of hypothalamus pituitary adrenocorticol and autonomic nervous system biomakers

Early life exposure to stress such as adverse childhood experiences has been suggested to cause changes in physiological processes and alteration in stress response magnitude which might have significant impact on health later in life. For this reason, detection of this altered stress response can be used as an indicator for future health. To date, there is no study that utilized this information to indicate future health. In order to detect the altered stress response, biomarkers that represent both Autonomic Nervous System (ANS) and Hypothalamic-Pituitary-Adrenocorticol (HPA) is 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. Even though many studies used multiple biomarkers to measure the stress response, the results for each biomarker were analysed separately. Therefore, this study fuses the biomarker that represents both ANS and HPA as a single measure, proposes a new method to classify the stress response based on adverse childhood experience in the form of stress response index as a future health indicator. Electrocardiograph, blood pressure, pulse rate and Salivary Cortisol (SCort) were collected from 23 participants, 12 participants who had adverse childhood experience while the remaining 11 act as the control group. The recording session was done during a Paced Auditory Serial Addition Test (PASAT). HRV features were then extracted from the electrocardiograph (ECG) using time, frequency, time-frequency analysis, and wavelet transform. Following this, genetic algorithm was implemented to select a subset of 12 HRV features from 83 features. Next, the selected HRV features were combined with other biomarkers using parallel and serial fusion for performance comparison. Using Support Vector Machine (SVM), results showed that fused feature of the parallel fusion, so-called Euclidean distance (ed), demonstrated the highest performance with 80.0% accuracy, 83.3% sensitivity and 78.3% specificity. Finally, the fused feature of the Euclidean distance was fed into SVM in order to model the stress response index as an indicator for future health. This index was validated using all samples and achieved 91.3% accuracy. From this study, a new method based on HRV-SCort biomarker using Euclidean distance and SVM named as ed-SVM was proven to be an effective method to classify the stress response and could further be used to model a stress response index. This index can then be benefited as an indicator for future health to improve the health care management in adulthood

Stress response index for adverse childhood experience based on fusion of hypothalamus pituitary adrenocorticol and autonomic nervous system biomarkers

Early life exposure to stress such as adverse childhood experiences has been suggested to cause changes in physiological processes and alteration in stress response magnitude which might have significant impact on health later in life. For this reason, detection of this altered stress response can be used as an indicator for future health. To date, there is no study that utilized this information to indicate future health. In order to detect the altered stress response, biomarkers that represent both Autonomic Nervous System (ANS) and Hypothalamic-Pituitary-Adrenocorticol (HPA) is 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. Even though many studies used multiple biomarkers to measure the stress response, the results for each biomarker were analysed separately. Therefore, this study fuses the biomarker that represents both ANS and HPA as a single measure, proposes a new method to classify the stress response based on adverse childhood experience in the form of stress response index as a future health indicator. Electrocardiograph, blood pressure, pulse rate and Salivary Cortisol (SCort) were collected from 23 participants, 12 participants who had adverse childhood experience while the remaining 11 act as the control group. The recording session was done during a Paced Auditory Serial Addition Test (PASAT). HRV features were then extracted from the electrocardiograph (ECG) using time, frequency, time-frequency analysis, and wavelet transform. Following this, genetic algorithm was implemented to select a subset of 12 HRV features from 83 features. Next, the selected HRV features were combined with other biomarkers using parallel and serial fusion for performance comparison. Using Support Vector Machine (SVM), results showed that fused feature of the parallel fusion, so-called Euclidean distance (ed), demonstrated the highest performance with 80.0% accuracy, 83.3% sensitivity and 78.3% specificity. Finally, the fused feature of the Euclidean distance was fed into SVM in order to model the stress response index as an indicator for future health. This index was validated using all samples and achieved 91.3% accuracy. From this study, a new method based on HRV-SCort biomarker using Euclidean distance and SVM named as ed-SVM was proven to be an effective method to classify the stress response and could further be used to model a stress response index. This index can then be benefited as an indicator for future health to improve the health care management in adulthood