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

Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review

: The most critical burden for People with Epilepsy (PwE) is represented by seizures, the unpredictability of which severely impacts quality of life. The design of real-time warning systems that can detect or even predict ictal events would enhance seizure management, leading to high benefits for PwE and their caregivers. In the past, various research works highlighted that seizure onset is anticipated by significant changes in autonomic cardiac control, which can be assessed through heart rate variability (HRV). This manuscript conducted a scoping review of the literature analyzing HRV-based methods for detecting or predicting ictal events. An initial search on the PubMed database returned 402 papers, 72 of which met the inclusion criteria and were included in the review. These results suggest that seizure detection is more accurate in neonatal and pediatric patients due to more significant autonomic modifications during the ictal transitions. In addition, conventional metrics are often incapable of capturing cardiac autonomic variations and should be replaced with more advanced methodologies, considering non-linear HRV features and machine learning tools for processing them. Finally, studies investigating wearable systems for heart monitoring denoted how HRV constitutes an efficient biomarker for seizure detection in patients presenting significant alterations in autonomic cardiac control during ictal events

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

Automatic Detection of Epileptic Seizures in Neonatal Intensive Care Units through EEG, ECG and Video Recordings: A Survey

In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is of utmost importance for a timely, effective and efficient clinical intervention. The continuous video electroencephalogram (v-EEG) is the gold standard for monitoring neonatal seizures, but it requires specialized equipment and expert staff available 24/24h. The purpose of this study is to present an overview of the main Neonatal Seizure Detection (NSD) systems developed during the last ten years that implement Artificial Intelligence techniques to detect and report the temporal occurrence of neonatal seizures. Expert systems based on the analysis of EEG, ECG and video recordings are investigated, and their usefulness as support tools for the medical staff in detecting and diagnosing neonatal seizures in NICUs is evaluated. EEG-based NSD systems show better performance than systems based on other signals. Recently ECG analysis, particularly the related HRV analysis, seems to be a promising marker of brain damage. Moreover, video analysis could be helpful to identify inconspicuous but pathological movements. This study highlights possible future developments of the NSD systems: a multimodal approach that exploits and combines the results of the EEG, ECG and video approaches and a system able to automatically characterize etiologies might provide additional support to clinicians in seizures diagnosis

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