Publications

Surface electromyography (sEMG) can be used to measure the electrical activity of the respiratory muscles. The possible applications of sEMG span from patients suffering from acute respiratory failure to patients receiving chronic home mechanical ventilation, to evaluate muscle function, titrate ventilatory support and guide treatment. However, sEMG is mainly used as a monitoring tool for research and its use in clinical practice is still limited-in part due to a lack of standardization and transparent reporting. During this round table meeting, recommendations on data acquisition, processing, interpretation, and potential clinical applications of respiratory sEMG were discussed. This paper informs the clinical researcher interested in respiratory muscle monitoring about the current state of the art on sEMG, knowledge gaps and potential future applications for patients with respiratory failure.

JTD Keywords: Acute respiratory failure, Artificial ventilation, Asthmatic-children, Breathing muscle, Clinical monitoring, Clinical practice, Clinical research, Consensus development, Data interpretation, Disease exacerbation, Drive, Electrode positioning, Electrode removal, Electromyography, Force, Home care, Human, Human diaphragm, Humans, Information processing, Inspiratory muscle training, Inspiratory muscles, Intensive care unit, Knowledge gap, Long term care, Mechanical ventilation, Medical procedures, Muscle contraction, Muscle fatigue, Muscle function, Muscle training, Muscle, skeletal, Muscle-activity, Noninvasive ventilation, Patient monitoring, Patient-ventilator asynchrony, Physiology, Prognosis, Quality of life, Reporting and data system, Respiratory failure, Respiratory muscles, Review, Severe exacerbations, Signal processing, Skeletal muscle, Standardization, Surface electromyography, Time factor

Low-cost gas sensors allow for large-scale spatial monitoring of air quality in the environment. However they require calibration before deployment. Methods such as multivariate regression techniques have been applied towards sensor calibration. In this work, we propose instead, the use of deep learning methods, particularly, recurrent neural networks for predicting the gas concentrations based on the outputs of these sensors. This paper presents a first study of using Gated Recurrent Unit (GRU) neural network models for gas concentration prediction. The GRU networks achieve on average, a 44.69% and a 25.17% RMSE improvement in concentration prediction on a gas dataset when compared with Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models respectively. With the current advances in deep network hardware accelerators, these networks can be combined with the sensors for a compact embedded system suitable for edge applications.

JTD Keywords: Robot sensing systems, Predictive models, Logic gates, Gas detectors, Training, Temperature measurement, Support vector machines

Background and Purpose- Evidence suggests that therapy can be effective in recovering from aphasia, provided that it consists of socially embedded, intensive training of behaviorally relevant tasks. However, the resources of healthcare systems are often too limited to provide such treatment at sufficient dosage. Hence, there is a need for evidence-based, cost-effective rehabilitation methods. Here, we asked whether virtual reality-based treatment grounded in the principles of use-dependent learning, behavioral relevance, and intensity positively impacts recovery from nonfluent aphasia. Methods- Seventeen patients with chronic nonfluent aphasia underwent intensive therapy in a randomized, controlled, parallel-group trial. Participants were assigned to the control group (N=8) receiving standard treatment or to the experimental group (N=9) receiving augmented embodied therapy with the Rehabilitation Gaming System for aphasia. All Rehabilitation Gaming System for aphasia sessions were supervised by an assistant who monitored the patients but did not offer any elements of standard therapy. Both interventions were matched for intensity and materials. Results- Our results revealed that at the end of the treatment both groups significantly improved on the primary outcome measure (Boston Diagnostic Aphasia Examination: control group, P=0.04; experimental group, P=0.01), and the secondary outcome measure (lexical access-vocabulary test: control group, P=0.01; experimental group, P=0.007). However, only the Rehabilitation Gaming System for aphasia group improved on the Communicative Aphasia Log ( P=0.01). The follow-up assessment (week 16) demonstrated that while both groups retained vocabulary-related changes (control group, P=0.01; experimental group, P=0.007), only the Rehabilitation Gaming System for aphasia group showed therapy-induced improvements in language ( P=0.01) and communication ( P=0.05). Conclusions- Our results demonstrate the effectiveness of Rehabilitation Gaming System for aphasia for improving language and communication in patients with chronic aphasia suggesting that current challenges faced by the healthcare system in the treatment of stroke might be effectively addressed by augmenting traditional therapy with computer-based methods. Clinical Trial Registration- URL: https://www.clinicaltrials.gov . Unique identifier: NCT02928822.

JTD Keywords: Aphasia, Embodied training, Neurological rehabilitation, Virtual reality

Home hospitalization (HH) is presented as a healthcare alternative capable of providing high standards of care when patients no longer need hospital facilities. Although HH seems to lower healthcare costs by shortening hospital stays and improving patient's quality of life, the lack of continuous observation at home may lead to complications in some patients. Since blood tests have been proven to provide relevant prognosis information in many diseases, this paper analyzes the impact of different sampling methods on the prediction of HH outcomes. After a first exploratory analysis, some variables extracted from routine blood tests performed at the moment of HH admission, such as hemoglobin, lymphocytes or creatinine, were found to unmask statistically significant differences between patients undergoing successful and unsucessful HH stays. Then, predictive models were built with these data, in order to identify unsuccessful cases eventually needing hospital facilities. However, since these hospital admissions during HH programs are rare, their identification through conventional machine-learning approaches is challenging. Thus, several sampling strategies designed to face class imbalance were herein overviewed and compared. Among the analyzed approaches, over-sampling strategies, such as ROSE (Random Over-Sampling Examples) and conventional random over-sampling, showed the best performances. Nevertheless, further improvements should be proposed in the future so as to better identify those patients not benefiting from HH.

JTD Keywords: Hospitals, Blood, Training, Standards, Diseases, Prognostics and health management

Aging population is a major concern that is reflected in the increase of chronic diseases. Heart Failure (HF) is one of the most common chronic diseases of elderly people that is punctuated with acute episodes, which result in hospitalization. The periodic modulation of the amplitude of the breathing pattern is proved to be one of the multiple symptoms of an acute episode, and thus, the features extracted from its characterization contribute in the improvement of the first diagnosis of the clinical practice. The main objective of this study is to evaluate if the features extracted from the breathing pattern along with common clinical variables are reliable enough to detect Periodic Breathing (PB). A dataset of 44 elderly patients containing clinical information and a short record of electrocardiogram and respiratory flow signal was used to train two machine learning classification methods: Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). All the available clinical parameters within the dataset along with the parameters characterizing the respiratory pattern were used to classify the observations into two groups. SVM classification was optimized and performed using a = -8 and C = 10.04 giving an accuracy of 88.2 % sensitivity of 90 % and specificity of 85.7 % Similar results were achieved with LDA classifying with an accuracy of 82.4 %, a sensitivity of 81.8% and specificity of 83.3 % PB has been accurately detected using both classifiers.

JTD Keywords: Support vector machines, Feature extraction, Training, Senior citizens, Standards, Training data, Hospitals

Robotics has shown its potential not only in assisting the surgeon during an intervention but also as a tool for training and for surgical procedure's evaluation. Thus, robotics can constitute an extension of simulators that are based on the high capabilities of computer graphics. In addition, haptics has taken a first step in increasing the performance of current virtual reality systems based uniquely on computer simulation and their corresponding interface devices. As a further step in the field of training and learning in surgery, this work describes a robotic experimental workstation composed of robots and specific measuring devices, together with their corresponding control and monitoring strategies for orthopaedic surgery. Through a case study, humerus arthroplasty, experimental evaluation shows the possibilities of having a test bed available for repetitive and quantifiable trials, which make a reliable scientific comparison between different surgical strategies possible.

JTD Keywords: Surgical robotics, Training robotics, Optimisation of surgical procedures, Surgical techniques evaluation