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

Fetal movements recording system using accelerometer sensor

One of the compelling challenges in modern obstetrics is the monitoring fetal wellbeing. Physicians are gradually becoming cognizant of the relationship between fetal activity, movement, welfare, and future developmental progress. Previous works have developed few accelerometer-based systems to tackle issues related to ultrasound measurement, the provision of remote s1pport and self-managed monitoring of fetal movement during pregnancy. Though, many research questions on the optimal setup in terms of body-worn accelerometers, as well as signal processing and machine learning techniques used to detect fetal movement, are still open. In this work, a new fetal movement system recorder has been proposed. The proposed system has six accelerometer sensors and ARDUINO microcontroller. The device which is interfaced with the MATLAB signal process tool has been designed to record, display and store relevant sets of fetal movements. The sensors are to be placed on the maternal abdomen to record and process physical signals originating from the fetal. Comparison of data recorded from fetal movements with ultrasound and maternal perception technique gave the following results. An accuracy of 59.78%, 85.87%,and 97.83% was achieved using the maternal perception technique, fetal movement recording system, and ultrasound respectively. The findings show that the proposed fetal movement recording system has a better accuracy rate than maternal perception technique, and can be compared with ultrasound

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

Comparative analysis of preprocessing techniques for quantification of heart rate variability

In this paper, a comparative analysis of preprocessing techniques for quantification of heart rate variability (HRV) were performed. These preprocessing techniques are used to transform the Electrocardiogram (ECG) to HRV so that appropriate for spectral and non linear analysis. A number of preprocessing techniques were investigated in this study. In order to evaluate the performance of the preprocessing methods, the differences between the frequency spectrum of the HRV were measured by contrasting the merit indices. Among the preprocessing techniques studied, the result indicate that the utilization of heart rate values instead of heart period values in the derivation of HRV results in more accurate spectrum. Furthermore, the result support that the preprocessing technique based on the convolution of inverse interval values with the rectangular window and the cubic interpolation of inverse interval values are efficient methods for quantification of HRV

New learning algorithm based on Hidden Markov Model (HMM) as stochastic modelling for pattern calssification

This study investigates the use discriminative training methods of minimum classification error (MCE) to estimate the parameter of hidden Markov model (HMM). The conventional training of HMM is based on the maximum likelihood estimation (MLE) which aims to model the true probabilistic distribution of the data in term of maximizing the likelihood. This requires sufficient training data and correct choice of probabilistic models, which in reality hardly achievable. The insufficient training data and incorrect modeling assumption of HMM often yield an incorrect and unreliable model. Instead of learning the true distribution, the MCE based training targeted to minimizing the probability of error is used to obtain optimal Bayes classification. The central idea of MCE based training is to define a continuous, differentiable loss function to approximate the actual performance error rate. Gradient based optimization methods can be used to minimize this loss. In this study the first order online generalized probabilistic descent is used as optimization methods. The continuous density HMM is used as the classifier structure in the MCE framework. The MCE based training is evaluated on speaker-independent Malay isolated digit recognition. The MCE training achieves the classification accuracy of 96.4% compared to 96.1% of using MLE with small improvement rate of 0.31%. The small vocabulary is unable to reflect the performance comparison of the two methods, the MLE training given sufficient training data is sufficient to provide optimal classification accuracy. Future work will extend the evaluation on difficult classification task such as phoneme classification, to better access the discriminative ability of the both methods

Investigating frequency contents of capnogram using fast fourier transform (FFT) and autoregressive modelling

In this study, the frequency contents of capnogram were investigated. Capnogram is the graphical output of capnography that represents the different changes in expiratory volume. Capnography is generally used for the monitoring of carbon dioxide (CO2) level during respiration. This method is not only simple, non- invasive and relatively inexpensive, but also mandated or recommended for patient monitoring during clinical procedures by medical societies representing anaesthesiology, cardiology, critical care, paediatrics, respiratory care and emergency medicine. Hence, the signal processing and analysis of capnogram will help in understanding its nature for the diagnosis and prognosis of a variety of respiratory disorders. It should be noted that till now there is no attempt to investigate the frequency contents of capnogram. Therefore, we investigated the frequency properties of capnogram to lead towards better and more accurate diagnostic algorithms related to respiratory malady. Fast Fourier transform (FFT) and autoregressive (AR) modelling-Burg method were used to calculate the power spectral density (PSD) in both normal and asthmatic capnograms, and the preliminary results showed that the frequency properties of the capnograms were significant to distinguish between asthmatic and non-asthmatic patients. These results revealed the potential of using the frequency contents of capnogram as a diagnostic tool or indicator, thus significantly helping medical practitioners involved in respiratory care

Heart rate variability time-frequency analysis for newborn seizure detection

The identification of newborn seizures requires the processing of a number of physiological signals routinely recorded from patients, including the EEG and ECG, as well as EOG and respiration signals. Most existing studies have focused on using the EEG as the sole information source in automatic seizure detection. Some of these studies concluded that the information obtained from the EEG should be supplemented by other information obtained from other recorded physiological signals. This chapter documents an approach that uses the ECG as the basis for seizure detection and explores how such approach could be combined with the EEG based methodologies to achieve a robust automatic seizure detector

Development of low cost and robust nursing calling device for hospitalized patients

Patient care is considered in top priority for all clinics and hospitals. Hence it is necessary to provide a dedicated line of a mediator between nurse and patient to provide excellent patient safety and to minimize the medical errors. Till date, traditional nursing calling system was used for this purpose which limits the patient information until the nurse’s chamber and outcomes of the system depended on the nurse that sometimes fail due to unavailability or shortage of manpower. It may lead to patient death too. Therefore, this study presents a low cost and robust nursing calling system. The proposed system consists of five parts: 1) patient switch, 2) acknowledgment switch, 3) nurse center station board, 4) administrator board and 5) control board. Whenever a patient pressed the button, the signal enabled the patient’s room and bed number along with demographic details in nurse chamber for two minutes. If unattended by nurses, it passes the signal to the administrator or physician room of the hospital for the further action. In addition, it is also capable of measuring the response time delivered by the nurse to the patient which is useful for the hospital management. Specially, it was designed for those patients who have been shifted from the ICU to General Ward for the better care of them. The total cost of the device is 15$ for each bed which shows the cost effectiveness of the device. Our developed device a step ahead of existing technology by technology for the ease of patients