A Self-Attention Deep Neural Network Regressor for real time blood glucose estimation in paediatric population using physiological signals

With the advent of modern digital technology, the physiological signals (such as electrocardiogram) are being acquired from portable wearable devices which are being used for non-invasive chronic disease management (such as Type 1 Diabetes). The diabetes management requires real-time assessment of blood glucose which is cumbersome for paediatric population due to clinical complexity and invasiveness. Therefore, real-time non-invasive blood glucose estimation is now pivotal for effective diabetes management. In this paper, we propose a Self-Attention Deep Neural Network Regressor for real-time non-invasive blood glucose estimation for paediatric population based on automatically extracted beat morphology. The first stage performs Morphological Extractor based on Self-Attention based Long Short-Term Memory driven by Convolutional Neural Network for highlighting local features based on temporal context. The second stage is based on Morphological Regressor driven by multilayer perceptron with dropout and batch normalization to avoid overfitting. We performed feature selection via logit model followed by Spearman's correlation among features to avoid feature redundancy. We trained as tested our model on publicly available MIT/BIH-Physionet databases and physiological signals acquired from a T1D paediatric population. We performed our evaluation via Clarke's Grid error to analyse estimation accuracy on range of blood values under different glycaemic conditions. The results show that our tool outperformed existing regression models with 89% accuracy under clinically acceptable range. The proposed model based on beat morphology significantly outperformed models based on HRV features

Robotic Technology in Pediatric Neurorehabilitation. A Pilot Study of Human Factors in an Italian Pediatric Hospital

The introduction of robotic neurorehabilitation among the most recent technologies in pediatrics represents a new opportunity to treat pediatric patients. This study aims at evaluating the response of physiotherapists, patients and their parents to this new technology. The study considered the outcomes of technological innovation in physiotherapists (perception of the workload, satisfaction), as well as that in patients and their parents (quality of life, expectations, satisfaction) by comparing the answers to subjective questionnaires of those who made use of the new technology with those who used the traditional therapy. A total of 12 workers, 46 patients and 47 parents were enrolled in the study. Significant differences were recorded in the total workload score of physiotherapists who use the robotic technology compared with the traditional therapy (p < 0.001). Patients reported a higher quality of life and satisfaction after the use of the robotic neurorehabilitation therapy. The parents of patients undergoing the robotic therapy have moderately higher expectations and satisfaction than those undergoing the traditional therapy. In this pilot study, the robotic neurorehabilitation technique involved a significant increase in the patients' and parents' expectations. As it frequently happens in the introduction of new technologies, physiotherapists perceived a greater workload. Further studies are needed to verify the results achieved

Case Studies on the Use of Sentiment Analysis to Assess the Effectiveness and Safety of Health Technologies: A Scoping Review

A health technology assessment (HTA) is commonly defined as a multidisciplinary approach used to evaluate medical, social, economic, and ethical issues related to the use of a health technology in a systematic, transparent, unbiased, robust manner. To help inform HTA recommendations, the surveillance of social media platforms can provide important insights to the clinical community and to decision makers on the effectiveness and safety of the use of health technologies on a patient. A scoping review of the published literature was performed to gain some insight on the accuracy and automation of sentiment analysis (SA) used to assess public opinion on the use of health technologies. A literature search of major databases was conducted. The main search concepts were SA, social media, and patient perspective. Among the 1,776 unique citations identified, 12 studies that described the use of SA methods to evaluate public opinion on or experiences with the use of health technologies as posted on social media platforms were included. The SA methods used were either lexicon-or machine learning-based. Two studies focused on medical devices, three examined HPV vaccination, and the remaining studies targeted drug therapies. Due to the limitations and inherent differences among SA tools, the outcomes of these applications should be considered exploratory. The results of our study can initiate discussions on how the automation of algorithms to interpret public opinion of health technologies should be further developed to optimize the use of data available on social media

Are the variations in ECG morphology associated to different blood glucose levels? implications for non-invasive glucose monitoring for T1D paediatric patients

Recent clinical trials and real-world studies highlighted those variations in ECG waveforms and HRV recurrently occurred during hypoglycemic and hyperglycemic events in patients with diabetes. However, while several studies have been carried out for adult age, there is lack of evidence for paediatric patients. The main aim of the study is to identify the correlations of variations in ECG Morphology waveforms with blood glucose levels in a paediatric population. Methods: T1D paediatric patients who use CGM were enrolled. They wear an additional non-invasive wearable device for recording physiological data and respiratory rate. Glucose metrics, ECG parameters and HRV features were collected, and Wilcoxon rank-sum test and Spearman’s correlation analysis were used to explore if different levels of blood glucose were associated to ECG morphological changes. Results: Results showed that hypoglycaemic events in paediatric patients with T1D are strongly associated with variations in ECG morphology and HRV. Conclusions: Results showed the opportunity of using the ECG as a non-invasive adding instrument to monitor the hypoglycaemic events through the integration of the ECG continuous information with CGM data. This innovative approach represents a promising step forward in diabetes management, offering a more comprehensive and effective means of detecting and responding to critical changes in glucose levels

Human factor of The Use Of Robotic Technology In Pediatric neurorehabilitation

Abstract Robotic technology represents a new rehabilitation opportunity that, with an approach similar to a video game, increases the motivation to treat children and seems able to activate brain plasticity, at the basis of the functional recovery due to its interactivity and intensity of the training experience. Literature reports few studies that evaluate the ergonomic aspects of devices for neurorehabilitation, from the point of view of both the operator and the patient. Similar studies in the pediatric field are rare. This study aims to evaluate the response of workers, patients and their parents to this new technology. The study considered the response of the workers (perception of the workload, satisfaction), that of the patients and their parents (expectations, benefits) by comparing the answers to subjective questionnaires of those who made use of the new technology with those who used the traditional technique. Twelve workers, 46 patients and 47 parents were enrolled in the study. Significant differences were recorded in the total workload score of operators who use the robotic technology compared to the traditional therapy (p &amp;lt; 0.001). Patients reported a higher quality of life and satisfaction after the use of robotic neurorehabilitation therapy. The parents of patients undergoing robotic therapy have moderately higher expectations and satisfaction than those undergoing traditional therapy. None of the parameters checked was worse in the new therapeutic technique than in the traditional one. In this pilot study, the robotic neurorehabilitation technique induced an increase in the expectations and satisfaction of patients and their parents. As is frequent in the introduction of new technologies, workers perceived a greater workload. Subsequent studies are needed to verify the results achieved. This study is being developed in an Italian pediatric hospital, with the collaboration and funding of the Italian Ministry of Health, coauthor of this study. Key messages Robotic therapy presents a higher workload compared to traditional one. Robotic technique induced an increase in quality of life of patients and in expectations and satisfaction of their parents. </jats:sec

Scanning conditions in functional connectivity magnetic resonance imaging: how to standardise resting-state for optimal data acquisition and visualisation?

Functional connectivity magnetic resonance imaging (fcMRI), performed during resting wakefulness without tasks or stimulation, is a non-invasive technique to assess and visualise functional brain networks in vivo. Acquisition of resting-state imaging data has become increasingly common in longitudinal studies to investigate brain health and disease. However, the scanning protocols vary considerably across different institutions creating challenges for comparability especially for the interpretation of findings in patient cohorts and establishment of diagnostic or prognostic imaging biomarkers. The aim of this chapter is to discuss the effect of two experimental conditions (i.e. a low cognitive demand paradigm and a pure resting-state fcMRI) on the reproducibility of brain networks between a baseline and a follow-up session, 30 (±5) days later, acquired from 12 right-handed volunteers (29 ± 5 yrs). A novel method was developed and used for a direct statistical comparison of the test-retest reliability using 28 well-established functional brain networks. Overall, both scanning conditions produced good levels of test-retest reliability. While the pure resting-state condition showed higher test-retest reliability for 18 of the 28 analysed networks, the low cognitive demand paradigm produced higher test-retest reliability for 8 of the 28 brain networks (i.e. visual, sensorimotor and frontal areas); in 2 of the 28 brain networks no significant changes could be detected. These results are relevant to planning of longitudinal studies, as higher test-retest reliability generally increases statistical power. This work also makes an important contribution to neuroimaging where optimising fcMRI experimental scanning conditions, and hence data visualisation of brain function, remains an on-going topic of interest. In this chapter, we provide a full methodological explanation of the two paradigms and our analysis so that readers can apply them to their own scanning protocols