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

Glycaemic variability-based classification of impaired glucose tolerance vs. type 2 diabetes using continuous glucose monitoring data

Many glycaemic variability (GV) indices extracted from continuous glucose monitoring systems data have been proposed for the characterisation of various aspects of glucose concentration profile dynamics in both healthy and non-healthy individuals. However, the inter-index correlations have made it difficult to reach a consensus regarding the best applications or a subset of indices for clinical scenarios, such as distinguishing subjects according to diabetes progression stage. Recently, a logistic regression-based method was used to address the basic problem of differentiating between healthy subjects and those affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D) in a pool of 25 GV-based indices. Whereas healthy subjects were classified accurately, the distinction between patients with IGT and T2D remained critical. In the present work, by using a dataset of CGM time-series collected in 62 subjects, we developed a polynomial-kernel support vector machine-based approach and demonstrated the ability to distinguish between subjects affected by IGT and T2D based on a pool of 37 GV indices complemented by four basic parameters—age, sex, BMI, and waist circumference—with an accuracy of 87.1%.Peer reviewe

DEVELOPMENT AND CROSS-VALIDATION OF A PREDICTION EQUATION FOR ESTIMATING STEP COUNT IN INDIVIDUALS WITH TRANSTIBIAL AMPUTATION

Outcome measures can be utilized to assess physical function in controlled settings, but do not provide a comprehensive view of free-living mobility for individuals with transtibial amputation (TTA). We sought to expand upon established clinical-based outcome measures by developing and cross validating two equations for predicting daily steps. The relationship between health state predictors and performance on 1) the Timed Up and Go (TUG) Test, and 2) the Prosthetic Limb User’s Survey of Mobility (PLUS-M) was also assessed via the model predictions. Adults with TTA were assigned activPAL and Fitbit accelerometers to wear for seven days. Participant data were randomly separated into training (n = 80) and testing (n = 26) groups. LASSO regression with 3-fold cross validation was implemented to construct each equation according to a participant’s health state, TUG Test, L Test of Functional Mobility, and PLUS-M data. Each equation’s validity was assessed in the testing group. An inverse relationship was noted between daily steps and TUG Test performance and higher PLUS-M T-scores were associated with greater daily steps. The equation overestimated steps for those with significantly low daily steps and underestimated steps for those with significantly high daily steps, which is to be expected given the nature of linear regression. We also assessed the validity of the Fitbit Inspire 3 for assessing steps among individuals with TTA. Daily step data were compared between the Fitbit Inspire 3 and the activPAL 3. The Fitbit overestimated physical activity by estimating higher daily steps compared to the activPAL. Because of the significant mean differences between the devices, the activPAL and Fitbit are not interchangeable for estimating steps in this group. The results will be interpreted and explored in the context of prosthetic rehabilitation and underscore the importance of personalized mobility assessments and interventions aimed at improving the free-living mobility of individuals with TTA

Computing resources sensitive parallelization of neural neworks for large scale diabetes data modelling, diagnosis and prediction

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Diabetes has become one of the most severe deceases due to an increasing number of diabetes patients globally. A large amount of digital data on diabetes has been collected through various channels. How to utilize these data sets to help doctors to make a decision on diagnosis, treatment and prediction of diabetic patients poses many challenges to the research community. The thesis investigates mathematical models with a focus on neural networks for large scale diabetes data modelling and analysis by utilizing modern computing technologies such as grid computing and cloud computing. These computing technologies provide users with an inexpensive way to have access to extensive computing resources over the Internet for solving data and computationally intensive problems. This thesis evaluates the performance of seven representative machine learning techniques in classification of diabetes data and the results show that neural network produces the best accuracy in classification but incurs high overhead in data training. As a result, the thesis develops MRNN, a parallel neural network model based on the MapReduce programming model which has become an enabling technology in support of data intensive applications in the clouds. By partitioning the diabetic data set into a number of equally sized data blocks, the workload in training is distributed among a number of computing nodes for speedup in data training. MRNN is first evaluated in small scale experimental environments using 12 mappers and subsequently is evaluated in large scale simulated environments using up to 1000 mappers. Both the experimental and simulations results have shown the effectiveness of MRNN in classification, and its high scalability in data training. MapReduce does not have a sophisticated job scheduling scheme for heterogonous computing environments in which the computing nodes may have varied computing capabilities. For this purpose, this thesis develops a load balancing scheme based on genetic algorithms with an aim to balance the training workload among heterogeneous computing nodes. The nodes with more computing capacities will receive more MapReduce jobs for execution. Divisible load theory is employed to guide the evolutionary process of the genetic algorithm with an aim to achieve fast convergence. The proposed load balancing scheme is evaluated in large scale simulated MapReduce environments with varied levels of heterogeneity using different sizes of data sets. All the results show that the genetic algorithm based load balancing scheme significantly reduce the makespan in job execution in comparison with the time consumed without load balancing.This work is funded by the EPSRC and China Market Association

Risk assessment tool for diabetic neuropathy.

Peripheral neuropathy is one of the serious complications of diabetes. Symptoms such as tingling and loss of touch sensation are commonly associated with the early stages of neuropathy causing numbness in the feet. Early detection of this condition is necessary in order to prevent the progression of the disease. Out of many detection techniques vibration perception is becoming the gold standard for neuropathy assessment. Devices like tuning fork, Biothesiometer and Neurothesiometer use this technology but require an operator to record and manually interpret the results. The results are user-dependent and are not consistent. To overcome these limitations, a platform-based device “VibraScan” was developed that can be self-operated and results displayed on a user interface. The development of the device is based on studying the effect of the vibration on the human subject by identifying the receptors responsible for sensation. The requirement of generating vibration was achieved by selecting a specific actuator that creates vibration perpendicular to the contact surface. The battery operated VibraScan is wirelessly controlled by software to generate vibration for determining the vibration perception threshold (VPT). Care has been taken while developing the user interface for human safety with the vibration intensity. The device can be operated without any assistance and results are automatically interpreted in terms of severity level indicated similar to the traffic-light classification. In order to provide consistent results with the existing devices a study was undertaken between Neurothesiometer and VibraScan with 20 healthy subjects. The results were compared using Bland-Altman plot and a close agreement was found between the two measurements. VibraScan accurately measures VPT based on the perceived vibration threshold, however, it does not predict any risk associated with neuropathy. In order to supplement this device with the progression of neuropathy a risk assessment tool was developed for automated prediction of neuropathy based on the clinical history of patients. The smart tool is based on the research related to the risk factors of diabetic neuropathy which was studied and analysed using summarised patient data. Box-Cox regression was used with the response variable (VPT) and a set of clinical variables as potential predictors. Significant predictors were: age, height, weight, urine albumin to creatinine ratio (ACR), HbA1c, cholesterol and duration of diabetes. Ordinary Least Squares Regression was then used with logarithmic (VPT) and the significant predictor set (Box-Cox transformed) to obtain additional fit estimates. With the aim to improving the precision of VPT prediction, a simulated patient data set (n = 4158) was also generated using the mean and the covariance of the original patient variables, but with reduced standard errors. For clinical or patient use, providing direct knowledge of VPT was considered less helpful than providing a simple risk category corresponding to a range of VPT values. To achieve this, the continuous scale VPT was recoded into three categories based on the following clinical thresholds in volts (V): low risk (0 to 20.99 V), medium risk (21 to 30.99 V) and high risk (≥ 31 V). Ordinal Logistic Regression was then used with this categorical outcome variable to confirm the original predictor set. Having established the effectiveness of this “classical” baseline, attention turned to Neural Network modelling. This showed that a carefully tuned Neural Network based Proportional Odds Model (NNPOM) could achieve a classification success >70%, somewhat higher than that obtained with the classical modelling. A version of this model was implemented in the VibraScan risk assessment tool. Integrating VibraScan and the risk assessment software has created a comprehensive diagnostic tool for diabetic neuropathy

Heart rate and blood pressure variability : association with white matter lesions and cognitive function following stroke

Dementia presents a significant health care burden. Older post-stroke patients suffer high rates of dementia. Subcortical ischaemia may be an important mechanism of cognitive decline, particularly in older patients with cerebrovascular disease. It is hypothesised that abnormal heart rate and blood pressure variability will increase white matter lesion volume through hypoperfusion. This may lead to a subcortical pattern of cognitive decline characterised for example by deficits in attention and concentration. Stroke patients aged > 75 years and free of dementia had a series of cardiovascular autonomic, brain imaging and neuropsychometric investigations performed more than three months following incident stroke. Annual neuropsychometric assessment included CAMCOG score and measures of reaction time and concentration using a series of visual and numerical tasks presented on computer (Cognitive Drug Research Assessment System). Autonomic function is impaired in older stroke patients in the long term after stroke. These deficits are weakly associated with cross-sectional measures of sub-cortical performance but do not predict subsequent decline in cognitive function. Twenty-four hour blood pressure variability is associated with white matter disease and excessive nocturnal dipping is associated with impaired cognitive function. Again blood pressure variability does not help predict subsequent change in white matter lesion burden or cognitive function. This study provides limited support for the hypoperfusion theory of post-stroke cognitive impairment. However it does not indicate a role for heart rate and blood pressure variability in the mechanism of increasing white matter disease or decline in cognition in the two years following stroke.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Novel medical imaging technologies for processing epithelium and endothelium layers in corneal confocal images. Developing automated segmentation and quantification algorithms for processing sub-basal epithelium nerves and endothelial cells for early diagnosis of diabetic neuropathy in corneal confocal microscope images

Diabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the corneal epithelium nerve structures and the corneal endothelial cell can assist early diagnosis of this disease and other corneal diseases, which can lead to visual impairment and then to blindness. In this thesis, fully-automated segmentation and quantification algorithms for processing and analysing sub-basal epithelium nerves and endothelial cells are proposed for early diagnosis of diabetic neuropathy in Corneal Confocal Microscopy (CCM) images. Firstly, a fully automatic nerve segmentation system for corneal confocal microscope images is proposed. The performance of the proposed system is evaluated against manually traced images with an execution time of the prototype is 13 seconds. Secondly, an automatic corneal nerve registration system is proposed. The main aim of this system is to produce a new informative corneal image that contains structural and functional information. Thirdly, an automated real-time system, termed the Corneal Endothelium Analysis System (CEAS) is developed and applied for the segmentation of endothelial cells in images of human cornea obtained by In Vivo CCM. The performance of the proposed CEAS system was tested against manually traced images with an execution time of only 6 seconds per image. Finally, the results obtained from all the proposed approaches have been evaluated and validated by an expert advisory board from two institutes, they are the Division of Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar and the Manchester Royal Eye Hospital, Centre for Endocrinology and Diabetes, UK

Computing resources sensitive parallelization of neural neworks for large scale diabetes data modelling, diagnosis and prediction

Diabetes has become one of the most severe deceases due to an increasing number of diabetes patients globally. A large amount of digital data on diabetes has been collected through various channels. How to utilize these data sets to help doctors to make a decision on diagnosis, treatment and prediction of diabetic patients poses many challenges to the research community. The thesis investigates mathematical models with a focus on neural networks for large scale diabetes data modelling and analysis by utilizing modern computing technologies such as grid computing and cloud computing. These computing technologies provide users with an inexpensive way to have access to extensive computing resources over the Internet for solving data and computationally intensive problems. This thesis evaluates the performance of seven representative machine learning techniques in classification of diabetes data and the results show that neural network produces the best accuracy in classification but incurs high overhead in data training. As a result, the thesis develops MRNN, a parallel neural network model based on the MapReduce programming model which has become an enabling technology in support of data intensive applications in the clouds. By partitioning the diabetic data set into a number of equally sized data blocks, the workload in training is distributed among a number of computing nodes for speedup in data training. MRNN is first evaluated in small scale experimental environments using 12 mappers and subsequently is evaluated in large scale simulated environments using up to 1000 mappers. Both the experimental and simulations results have shown the effectiveness of MRNN in classification, and its high scalability in data training. MapReduce does not have a sophisticated job scheduling scheme for heterogonous computing environments in which the computing nodes may have varied computing capabilities. For this purpose, this thesis develops a load balancing scheme based on genetic algorithms with an aim to balance the training workload among heterogeneous computing nodes. The nodes with more computing capacities will receive more MapReduce jobs for execution. Divisible load theory is employed to guide the evolutionary process of the genetic algorithm with an aim to achieve fast convergence. The proposed load balancing scheme is evaluated in large scale simulated MapReduce environments with varied levels of heterogeneity using different sizes of data sets. All the results show that the genetic algorithm based load balancing scheme significantly reduce the makespan in job execution in comparison with the time consumed without load balancing.EThOS - Electronic Theses Online ServiceEPSRCChina Market AssociationGBUnited Kingdo

Cardiovascular data analytics for real time patient monitoring

Improvements in wearable sensor devices make it possible to constantly monitor physiological parameters such as electrocardiograph (ECG) signals for long periods. Remote patient monitoring with wearable sensors has an important role to play in health care, particularly given the prevalence of chronic conditions such as cardiovascular disease (CVD)—one of the prominent causes of morbidity and mortality worldwide. Approximately 4.2 million Australians suffer from long-term CVD with approximately one death every 12 minutes. The assessment of ECG features, especially heart rate variability (HRV), represents a non-invasive technique which provides an indication of the autonomic nervous system (ANS) function. Conditions such as sudden cardiac death, hypertension, heart failure, myocardial infarction, ischaemia, and coronary heart disease can be detected from HRV analysis. In addition, the analysis of ECG features can also be used to diagnose many types of life-threatening arrhythmias, including ventricular fibrillation and ventricular tachycardia. Non-cardiac conditions, such as diabetes, obesity, metabolic syndrome, insulin resistance, irritable bowel syndrome, dyspepsia, anorexia nervosa, anxiety, and major depressive disorder have also been shown to be associated with HRV. The analysis of ECG features from real time ECG signals generated from wearable sensors provides distinctive challenges. The sensors that receive and process the signals have limited power, storage and processing capacity. Consequently, algorithms that process ECG signals need to be lightweight, use minimal storage resources and accurately detect abnormalities so that alarms can be raised. The existing literature details only a few algorithms which operate within the constraints of wearable sensor networks. This research presents four novel techniques that enable ECG signals to be processed within the limitations of resource constraints on devices to detect some key abnormalities in heart function. - The first technique is a novel real-time ECG data reduction algorithm, which detects and transmits only those key points that are critical for the generation of ECG features for diagnoses. - The second technique accurately predicts the five-minute HRV measure using only three minutes of data with an algorithm that executes in real-time using minimal computational resources. - The third technique introduces a real-time ECG feature recognition system that can be applied to diagnose life threatening conditions such as premature ventricular contractions (PVCs). - The fourth technique advances a classification algorithm to enhance the performance of automated ECG classification to determine arrhythmic heart beats based on noisy ECG signals. The four novel techniques are evaluated in comparison with benchmark algorithms for each task on the standard MIT-BIH Arrhythmia Database and with data generated from patients in a major hospital using Shimmer3 wearable ECG sensors. The four techniques are integrated to demonstrate that remote patient monitoring of ECG using HRV and ECG features is feasible in real time using minimal computational resources. The evaluation show that the ECG reduction algorithm is significantly better than existing algorithms that can be applied within sensor nodes, such as time-domain methods, transformation methods and compressed sensing methods. Furthermore, the proposed ECG reduction is found to be computationally less complex for resource constrained sensors and achieves higher compression ratios than existing algorithms. The prediction of a common HRV measure, the five-minute standard deviation of inter-beat variations (SDNN) and the accurate detection of PVC beats was achieved using a Count Data Model, combined with a Poisson-generated function from three-minute ECG recordings. This was achieved with minimal computational resources and was well suited to remote patient monitoring with wearable sensors. The PVC beats detection was implemented using the same count data model together with knowledge-based rules derived from clinical knowledge. A real-time cardiac patient monitoring system was implemented using an ECG sensor and smartphone to detect PVC beats within a few seconds using artificial neural networks (ANN), and it was proven to provide highly accurate results. The automated detection and classification were implemented using a new wrapper-based hybrid approach that utilized t-distributed stochastic neighbour embedding (t-SNE) in combination with self-organizing maps (SOM) to improve classification performance. The t-SNE-SOM hybrid resulted in improved sensitivity, specificity and accuracy compared to most common hybrid methods in the presence of noise. It also provided a better, more accurate identification for the presence of many types of arrhythmias from the ECG recordings, leading to a more timely diagnosis and treatment outcome.Doctor of Philosoph