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

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 traumatic childhood experience based on the fusion of hypothalamus pituitary adrenocorticol and autonomic nervous system biomarkers

Stress occurring in the early days of an individual was often assumed to cause several health consequences. A number of reports indicated that having to deal with unfavourable events or distress situation at a young age could tweak stress responses leading to a broad spectrum of poor mental and physical health condition. Therefore, changes identified within stress response were recommended to be taken as a measure in regulating and managing such health situation. This study combines the biomarker that represents both autonomic nervous system (ANS) and hypothalamic-pituitary-adrenocorticol (HPA) as a single measure to classify the stress response based on traumatic childhood experience and propose a stress response index as a future health indicator. Electrocardiograph (ECG), blood pressure, pulse rate and salivary cortisol (SCort) were collected from 12 participants who had traumatic childhood experience while the remaining 11 acted as the control group. The recording session was done during a Paced Auditory Serial Addition Test (PASAT). HRV was then computed from the ECG and the HRV features were extracted. Next, the best HRV features were selected using Genetic Algorithm (GA). Biomarkers such as BP, PR and SCort were then integrated with 12 HRV features picked from GA. The integrations were conducted using two fusion methods which are Euclidean distance and serial fusion. The differences in reaction of the fused features were then identified. Based on the result, the Euclidean distance (ed) which is the fused feature by the parallel fusion, displayed the most efficient reaction with accuracy, sensitivity, and specificity at 80.0%, 83.3% and 78.3%, respectively. Support Vector Machine (SVM) was utilized to attain such result. The fused feature performance was then fed into SVM which produced indexes on stress responses. The result retrieved from these indexes acts as a measure in handling future health deliverability and perhaps could eventually enhance the health care platform for midlife individuals

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

Quantitative Comparison of Time Frequency Distribution for Heart Rate Variability Using Performance Measure

Heart Rate Variability (HRV) has been proposed as a promising non-invasive method to assess Autonomic Nervous System (ANS). The recent trend of analysing HRV, which is a non-stationary signal is using the Time Frequency (TF) analysis such as Time Frequency Distribution (TFD). However, the use of TFD is different for every application, therefore, comparison of TFD performance needs to be carried out to select the suitable TFD. The comparisons performed by previous studies were limited to visual comparison which is very subjective and could lead to error. Therefore, this paper presents an objective quantitative comparison using performance measure, M to select the suitable TFD that characterises HRV response during an Autonomic Function Test (AFT). The investigated TFDs are the Wigner Ville (WVD), Smoothed Pseudo Wigner Ville (SPWVD), Choi William (CWD), Spectrogram (SP), and recently introduced Modified B-Distribution (MBD). From the results, we conclude that MBD and SPWVD demonstrated the highest value of performance measure M, with

Heart Rate Variability Recording System Using Photoplethysmography Sensor

Heart rate variability (HRV) is a physiological measurement that can help to monitor and diagnose chronic diseases such as cardiovascular disease, depression, and psychological stress. HRV measurement is commonly extracted from the electrocardiography (ECG). However, ECG has bulky wires where it needs at least three surface electrodes to be placed on the skin. This may cause distraction during the recording and need longer time to setup. Therefore, photoplethysmography (PPG), a simple optical technique, was suggested to obtain heart rate. This study proposes to investigate the effectiveness of PPG recording and derivation of HRV for feature analysis. The PPG signal was preprocessed to remove all the noise and to extract the HRV. HRV features were collected using time-domain analysis (TA), frequency-domain analysis (FA) and nonlinear time-frequency analysis (TFA). Five out of 22 HRV features, which are HR, RMSSD, LF/HF, LFnu, and HFnu, showed high correlation (rho > 0.6 and prho < 0.05) in comparison to standard 5-min excerpt while producing significant difference (p-value < 0.05) during the stressing condition across all interval HRV excerpts. This simple yet accurate PPG recording system perhaps might useful to assess the HRV signal in a short time, and further can be used for the ANS assessment

Spectral analysis of HRV in the assessment of autonomic function on normal subject

Heart rate variability (HRV) has been proposed as a diagnostic tool in assessing autonomic function. This paper investigates HRV response of healthy subject during active postural test (APT) and cold pressor test (CPT) using AR spectral analysis. Electrocardiograph (ECG) was recorded from 30 young adults; 19 females and 11 males with age ranging from 20-30 years. Autoregressive (AR) estimated by Burg was then used to compute the power spectral density (PSD). LFnu, HFnu and LF/HF ratio show significant difference (p#60;0.05) comparing supine and stand for APT and supine and hand immersion in cold water for CPT. As a conclusion, the HRV response shows predominance of the parasympathetic during supine position and sympathetic predominance during standing and hand immersion in cold water

The effect of adverse childhood experience on heart rate variability and salivary cortisol

Adverse childhood exposure has been discovered might alter physiological processes such as cardiovascular stress response. When the body is in a stressful condition, it triggers two primary systems that are particularly involved in adapting the body to the stress: the Autonomic Nervous System (ANS) and the Hypothalamic-Pituitary-Adrenocorticol (HPA) axis. To detect the altered stress response, biomarkers that represent both systems: ANS and HPA 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. This study will investigate the stress response on individual who have had adverse childhood experience and no adverse childhood experience by using HRV and salivary cortisol. Electrocardiograph and salivary cortisol were collected from 23 healthy participants (age, 19 to 23 years old), 12 participants who had adverse childhood experience while the remaining 11 acted as the control group. The recording session was done during a Paced Auditory Serial Addition Test (PASAT). HRV was then computed from the ECG and the HRV features were extracted. From the result, it can be seen that irregular stress response detected by HRV and salivary cortisol was found associated with adverse childhood experience with moderate classification performance; accuracy 61.7% and 59.4% respectively. To achieve a better classifier performance, an approach to the fusion method for stress response detection of adverse childhood experience is proposed for the future study

Assessment of Heart Rate Variability Response in Children with Autism Spectrum Disorder using Machine Learning

Autism spectrum disorder (ASD) is a developmental disability that involves persistent challenges in social interaction, communication and behaviour. The purpose of this study is to apply a machine learning approach to differentiate between autistic and normal children and to evaluate the performance of different classifiers in the detection of autism disorder. Heart Rate Variability (HRV) analysis is one of the strategies used for ASD detection by assessing the autonomic nervous system (ANS), which serves as a biomarker for the autism phenotype. HRV can be derived from the photoplethysmogram (PPG). Logistic Regression, Linear Discriminant Analysis and a Cubic Support Vector Machine (SVM) were chosen to evaluate the performance of HRV features in differentiating between normal and autistic children. Three different combinations of features were selected out of 19 features in total. From the results, Logistic Regression was the best classifier to differentiate between autistic and normal children in a colour stimulus test, while Linear Discriminant Analysis was best suited in the baseline test. In conclusion, the machine learning approach could be an alternative method of making an early diagnosis of ASD in the near future