Preparation of 2D sequences of corneal images for 3D model building

A confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, medical practioners can extract clinical information on the state of health of the patient's cornea. In this work we are addressing problems associated with capturing and processing these images including blurring, non-uniform illumination and noise, as well as the displacement of images laterally and in the anterior–posterior direction caused by subject movement. The latter may cause some of the captured images to be out of sequence in terms of depth. In this paper we introduce automated algorithms for classification, reordering, registration and segmentation to solve these problems. The successful implementation of these algorithms could open the door for another interesting development, which is the 3D modelling of these sequences

In Vivo Confocal Microscopic Corneal Images in health and disease with an emphasis on extracting features and visual signatures for corneal diseases: A review study

There is an evolution in the demands of modern ophthalmology from descriptive findings to assessment of cellular level changes by using in vivo confocal microscopy. Confocal microscopy, by producing grey-scale images, enables a microstructural insight into the in vivo cornea in both health and disease, including epithelial changes, stromal degenerative or dystrophic diseases, endothelial pathologies, and corneal deposits and infections. Ophthalmologists use acquired confocal corneal images to identify health and disease states and then to diagnose which type of disease is affecting the cornea. This paper presents the main features of the healthy confocal corneal layers, and reviews the most common corneal diseases. It identifies the visual signature of each disease in the affected layer and extracts the main features of this disease in terms of intensity, certain regular shapes with both their size and diffusion, and some specific region of interest. These features will lead towards the development of a complete automatic corneal diagnostic system which predicts abnormalities in the confocal corneal data sets

Evaluating bad and good EEG segments based on extracted features: towards an automated understanding of infant behavior and attention

The field of brain computer interference has grown rapidly with the purpose of reading a human’s mind, generating a certain output, controlling objects with this output and having an automated understanding of human reactions and responses to the surrounding environment. Electroencephalography (EEG) may provide an insight into human behavior and attention. There is a huge need in different fields, e.g. psychology and medical, for an automated approach that helps the psychologist in dealing with the massive amount of data in a sensible period. This research study proposes an approach to extract some features from infant EEG signals and evaluate the effect of the bad or good EEG channels on different EEG segments. The achieved work will provide an insight about the employment of the most suitable features to represent the EEG data. The acquired infant EEG data will be deployed to build an objectively evaluated framework that has the ability to provide an automated understanding of the infants’ behavior, underpin the infant specialists in analyzing the infant attentions for stimuli within different environments

Predicting the Health Impacts of Commuting Using EEG Signal Based on Intelligent Approach

Commuting to work is an everyday activity for many which can have a significant effect on our health. Commuting on regular basis can be a cause of chronic stress which is linked to poor mental health, high blood pressure, heart rate, and exhaustion. This research investigates the neurophysiological and psychological impact of commuting in real-time, by analyzing brain waves and applying machine learning. The participants were healthy volunteers with mean age of 30 years. Portable electroencephalogram (EEG) data were acquired as a measure of stress level. EEG data were acquired from each participant using non-invasive NeuroSky MindWave headset for 5 continuous activities during their commute to work. This approach allowed effects to be measured during and following the period of commuting. The results indicate that whether the duration of commute was low or large, when participants were in a calm or relaxed state the bio-signal alpha band exceeded beta band whereas beta band was higher than alpha band when participants were stressed due to their commute. Very promising results have been achieved with an accuracy of 97.5% using Feed-forward neural network. This work focuses on the development of an intelligent model that helps to predict the impact of commuting on participants. In addition, the result obtained from the Positive and Negative Affect Schedule also suggests that participants experience a considerable rise in stress after their commute. For modelling of cognitive and semantic processes underlying social behavior, the most of the recent research projects are still based on individuals, while our research focuses on approaches addressing groups as a complete cohort. This study recorded the experience of commuters with a special focus on the use and limitation of emerging computing technologies in telehealth sensors.</p

Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis

Antibiotic-resistant bacteria have proliferated at an alarming rate as a result of the extensive use of antibiotics and the paucity of new medication research. The possibility that an antibiotic-resistant bacterial infection would progress to sepsis is one of the major collateral problems affecting people with this condition. 31,000 lives were lost due to sepsis in England with costs about two billion pounds annually. This research aims to develop and evaluate several classification approaches to improve predicting sepsis and reduce the tendency of underdiagnosis in computer-aided predictive tools. This research employs medical datasets for patients diagnosed with sepsis, and it analyses the efficacy of ensemble machine learning techniques compared to nonensemble machine learning techniques and the significance of data balancing and conditional tabular generative adversarial nets for data augmentation in producing reliable diagnosis. The average F Score obtained by the nonensemble models trained in this paper is 0.83 compared to the ensemble techniques average of 0.94. Nonensemble techniques, such as Decision Tree, achieved an F score of 0.90, an AUC of 0.90, and an accuracy of 90%. Histogram-basedgradient boosting classification tree achieved an F score of 0.96, an AUC of 0.96, and an accuracy of 95%, surpassing the other models tested. Additionally, when compared to the current state-of-the-art sepsis prediction models, the models developed in this study demonstrated higher average performance in all metrics, indicating reduced bias and improved robustness through data balancing and conditional tabular generative adversarial nets for data augmentation. The study revealed that data balancing and augmentation on the ensemble machine learning algorithms boost the efficacy of clinical predictive models and can help clinics decide which data types are most important when examining patients and diagnosing sepsis early through intelligent human-machine interface

Evaluating the Stressful Commutes Using Physiological Signals and Machine Learning Techniques

Stress can be described as an alteration in our body that can cause strain emotionally, physically, or psychologically. It is a reaction from our body to something that demands attention or exertion. It can be caused by various reasons depending on the physical or mental activity of the body. Commuting on a regular basis also acts as a source of stress. This research aims to explore the physiological effects of the commute with an application of a machine-learning algorithm. The data used in this research is collected from 45 healthy participants who commute to work on a regular basis. A multimodal dataset containing medical data like biosignals (heart rate, blood pressure, and EEG signal) plus responses obtained from the questionnaire PANAS. Evaluation is based on the performance metrics that include confusion matrix, ROC/AUC, and classification accuracy of the model. In this research, several machine learning algorithms are applied to design a model which can predict the effect of a commute. The results obtained from this research suggest that whether the interval of commute was small or large, there was a significant rise in stress levels including the bio-signals (electroencephalogram, blood pressure and heart rate) after the commute. The results obtained from the employed machine learning algorithms predict that heart rate difference before and after commute will correlate with EEG signals in participants who have self-reported to be stress after the commute. The random forest algorithm gave a very promising result with an accuracy of 91%, while the KNN and the SVM showed the accuracy of 78% and 80% respectively.</p

An Effective Knowledge-Based Modeling Approach towards a “Smart-School Care Coordination System” for Children and Young People with Special Educational Needs and Disabilities

There is a significant need for a computer-aided modeling, effective information analysis and ontology knowledge base models to support both special needs children and care providers. As this research work correlated to the symmetry scope, it proposes an innovative generic smart knowledge-based &ldquo;School Care Coordination System&rdquo; (SCCS), which is established on a novel holistic six-layered data management model. The development of the Smart-SCCS adopts a methodology of ontology engineering to transform the given theoretical unstructured special educational needs and disabilities (SEND) code of practice into a comprehensive knowledge representation and reasoning system. The intended purpose is to deliver a system that can coordinate and bring together education, health and social care services into a single application to meet the needs of children and young people (CYP) with SEND. Moreover, it enables coordination, integration and monitoring of education, health and social care activities between different actors (formal, informal and CYP in the education sector) involved in the school care process network to provide personalized care interventions based on a predefined care plan. The developed ontology knowledge-based model has been proven efficient and solved the enormous difficulties faced by schools and local authorities on a daily basis. It enabled the coordination of care and integration of information for CYP from different departments in health, social care and education. The developed model has received significant attention with great feedback from all the schools and the local authorities involved, showing its efficiency and robustness

Home-based transcranial direct current stimulation in bipolar depression:an open-label treatment study of clinical outcomes, acceptability and adverse events

BACKGROUND: Current treatments for bipolar depression have limited effectiveness, tolerability and acceptability. Transcranial direct current stimulation (tDCS) is a novel non-invasive brain stimulation method that has demonstrated treatment efficacy for major depressive episodes. tDCS is portable, safe, and individuals like having sessions at home. We developed a home-based protocol with real-time remote supervision. In the present study, we have examined the clinical outcomes, acceptability and feasibility of home-based tDCS treatment in bipolar depression.RESULTS: Participants were 44 individuals with bipolar disorder (31 women), mean age 47.27 ± 12.89 years, in current depressive episode of at least moderate severity (mean Montgomery Asberg Depression Rating Scale (MADRS) score 24.59 ± 2.64). tDCS was provided in bilateral frontal montage, F3 anode, F4 cathode, 2 mA, for 30 min, in a 6-week trial, for total 21 sessions, a follow up visit was conducted 5 months from baseline. Participants maintained their current treatment (psychotherapy, antidepressant or mood stabilising medication) or maintained being medication-free. A research team member was present by video conference at each session. 93.2% participants (n = 41) completed the 6-week treatment and 72.7% of participants (n = 32) completed the 5 month follow up. There was a significant improvement in depressive symptoms following treatment (mean MADRS 8.77 ± 5.37) which was maintained at the 5 month follow up (mean MADRS 10.86 ± 6.90), rate of clinical response was 77.3% (MADRS improvement of 50% or greater from baseline), and rate of clinical remission was 47.7% (MADRS rating of 9 or less). Acceptability was endorsed as "very acceptable" or "quite acceptable" by all participants. No participants developed mania or hypomania.CONCLUSIONS: In summary, home-based tDCS with real-time supervision was associated with significant clinical improvements and high acceptability in bipolar depression. Due to the open-label design, efficacy findings are preliminary.TRIAL REGISTRATION: ClinicalTrials.gov number NCT05436613 registered on 23 June 2022 https//www.CLINICALTRIALS: gov/study/NCT05436613.</p