Opisso E.,Institute Guttmann Neurorehabilitation Hospital |
Borau A.,Institute Guttmann Neurorehabilitation Hospital |
Rijkhoff N.J.M.,University of Aalborg
Journal of Neural Engineering | Year: 2011
The goal of this study was to investigate whether real-time external urethral sphincter (EUS) EMG-controlled dorsal genital nerve (DGN) stimulation can suppress undesired detrusor bladder contractions in patients with both neurogenic detrusor overactivity (NDO) and detrusor sphincter dyssynergia (DSD). Detrusor pressure (Pdet) and EUS EMG were recorded in 12 neurogenic patients who underwent two filling cystometries. The first one was without stimulation and was intended to confirm the NDO and DSD and to set the EMG detection threshold. The second one was with real-time EMG-controlled stimulation of DGNs. Two detection methods were analyzed to detect bladder contractions. The first method was a Kurtosis-scaled root mean square (RMS) detector and was used on-line. The second was a simple RMS detector and was used off-line. Of 12 patients included, 10 patients showed both NDO and DSD. In nine of these ten patients relevant EMG concomitant to detrusor activity was detected and stimulation could suppress at least one detrusor contraction. The second filling compared to the first one showed an increase of 84% in bladder capacity (p = 0.002) and a decrease of 106% in Pdet (p = 0.002). Nine false-positive detections occurred during the ten fillings with electrical stimulation. The mean increases of both time and Pdet between stimulation and bladder contraction onsets for method 1 were 1.8 s and 4 cmH2O and for method 2 were 0.9 s and 2 cmH2O, respectively. This study shows that EUS EMG can be used in real time to detect the onset of a bladder contraction. In combination with DGN stimulation has been shown to be feasible to suppress undesired bladder contractions and in turn to increase bladder capacity in subjects with both NDO and DSD. © 2011 IOP Publishing Ltd.
Garcia-Altes A.,Agencia dInformacio |
Perez K.,CIBER ISCIII |
Novoa A.,CIBER ISCIII |
Suelves J.M.,Agncia de Salut Pblica de Catalonia |
And 7 more authors.
Neuroepidemiology | Year: 2012
Background: Among traumatic injuries, spinal cord injuries (SCI) and traumatic brain injuries (TBI) are of major importance because of their epidemiological and economic impact on society. The overall objective of this study was to estimate the economic cost associated with people with SCI and TBI in Spain in 2007. Methods: A cost-of-illness analysis was performed, considering the perspective of society, using a 1-year time horizon. Medical costs, adaptation costs, material costs, administrative costs, and costs of police, firefighters and roadside assistance, productivity losses due to institutionalization and sick leave, as well as an estimate of productivity losses of carers, and productivity losses due to death were included. Results: The economic cost associated with people with SCI is between EUR 92,087,080.97 and 212,496,196.41 (USD 131 million and 302 million) according to the injury mechanism, and between EUR 1,079,223,688.66 and 3,833,752,692.78 (USD 1,536 million and 5,458 million) for people with TBI. Conclusions: There is an urgent need to develop effective interventions known to prevent SCI and TBI, and to evaluate their effectiveness and efficiency. Copyright © 2012 S. Karger AG, Basel.
Martinez-Moreno J.M.,Technical University of Madrid |
Martinez-Moreno J.M.,CIBER ISCIII |
Solana J.,Technical University of Madrid |
Solana J.,CIBER ISCIII |
And 12 more authors.
Studies in Health Technology and Informatics | Year: 2013
Cognitive impairment is the main cause of disability in developed societies. New interactive technologies help therapists in neurorehabilitation in order to increase patients' autonomy and quality of life. This work proposes Interactive Video (IV) as a technology to develop cognitive rehabilitation tasks based on Activities of Daily Living (ADL). ADL cognitive task has been developed and integrated with eye-tracking technology for task interaction and patients' performance monitoring. © 2013 The authors and IOS Press. All rights reserved.
Lobo-Prat J.,Institute Guttmann Neurorehabilitation Hospital |
Lobo-Prat J.,University of Twente |
Font-Llagunes J.M.,Polytechnic University of Catalonia |
Gomez-Perez C.,Institute Guttmann Neurorehabilitation Hospital |
And 2 more authors.
Computer Methods in Biomechanics and Biomedical Engineering | Year: 2014
Cervical spinal cord injury and acquired brain injury commonly imply a reduction in the upper extremity function which complicates, or even constrains, the performance of basic activities of daily living. Neurological rehabilitation in specialised hospitals is a common treatment for patients with neurological disorders. This study presents a practical methodology for the objective and quantitative evaluation of the upper extremity motion during an activity of daily living of those subjects. A new biomechanical model (with 10 rigid segments and 20 degrees of freedom) was defined to carry out kinematic, dynamic and energetic analyses of the upper extremity motion during a reaching task through data acquired by an optoelectronic system. In contrast to previous upper extremity models, the present model includes the analysis of the grasp motion, which is considered as crucial by clinicians. In addition to the model, we describe a processing and analysis methodology designed to present relevant summaries of biomechanical information to rehabilitation specialists. As an application case, the method was tested on a total of four subjects: three healthy subjects and one pathological subject suffering from cervical spinal cord injury. The dedicated kinematic, dynamic and energetic analyses for this particular case are presented. The resulting set of biomechanical measurements provides valuable information for clinicians to achieve a thorough understanding of the upper extremity motion, and allows comparing the motion of healthy and pathological cases. © 2014 © 2012 Taylor & Francis.
Marcano-Cedeno A.,Complutense University of Madrid |
Marcano-Cedeno A.,CIBER ISCIII |
Chausa P.,Complutense University of Madrid |
Chausa P.,CIBER ISCIII |
And 6 more authors.
Artificial Intelligence in Medicine | Year: 2013
Objective: The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials: The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3-5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results: The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions: The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence. © 2013 Elsevier B.V.
Palacios E.M.,August Pi i Sunyer Biomedical Research Institute |
Sala-Llonch R.,August Pi i Sunyer Biomedical Research Institute |
Sala-Llonch R.,University of Barcelona |
Junque C.,University of Barcelona |
And 4 more authors.
JAMA Neurology | Year: 2013
Importance The study of brain activity and connectivity at rest provides a unique opportunity for the investigation of the brain substrates of cognitive outcome after traumatic axonal injury. This knowledge may contribute to improve clinical management and rehabilitation programs. OBJECTIVE To study functional magnetic resonance imaging abnormalities in signal amplitude and brain connectivity at rest and their relationship to cognitive outcome in patients with chronic and severe traumatic axonal injury. DESIGN Observational study. SETTING University of Barcelona and Hospital Clinic de Barcelona, Barcelona, and Institut Guttmann-Neurorehabilitation Hospital, Badalona, Spain. PARTICIPANTS Twenty patients with traumatic brain injury (TBI) were studied, along with 17 matched healthy volunteers. INTERVENTIONS Resting-state functional magnetic resonance imaging and diffusion tensor imaging data were acquired. After exploring group differences in amplitude of low-frequency fluctuations (ALFF), we studied functional connectivity within the default mode network (DMN) by means of independent component analysis, followed by a dual regression approach and seed-based connectivity analyses. Finally, we performed probabilistic tractography between the frontal and posterior nodes of the DMN. MAIN OUTCOMES AND MEASURES Signal amplitude and functional connectivity during the resting state, tractography related to DMN, and the association between signal amplitudes and cognitive outcome. RESULTS Patients had greater ALFF in frontal regions, whichwas correlated with cognitive performance. Within the DMN, patients showed increased connectivity in the frontal lobes. Seed-based connectivity analyses revealed augmented connectivity within surrounding areas of the frontal and left parietal nodes of the DMN. Fractional anisotropy of the cingulate tract was correlated with increased connectivity of the frontal node of theDMNin patients with TBI. CONCLUSIONS AND RELEVANCE Increased ALFF is related to better cognitive performance in chronic TBI. The loss of structural connectivity produced by damage to the cingulum tract explained the compensatory increases in functional connectivity within the frontal node of the DMN.
Perez R.,Technical University of Madrid |
Costa U.,Institute Guttmann Neurorehabilitation Hospital |
Torrent M.,Research Center |
Solana J.,Technical University of Madrid |
And 5 more authors.
Sensors | Year: 2010
Here an inertial sensor-based monitoring system for measuring and analyzing upper limb movements is presented. The final goal is the integration of this motion-tracking device within a portable rehabilitation system for brain injury patients. A set of four inertial sensors mounted on a special garment worn by the patient provides the quaternions representing the patient upper limb's orientation in space. A kinematic model is built to estimate 3D upper limb motion for accurate therapeutic evaluation. The human upper limb is represented as a kinematic chain of rigid bodies with three joints and six degrees of freedom. Validation of the system has been performed by co-registration of movements with a commercial optoelectronic tracking system. Successful results are shown that exhibit a high correlation among signals provided by both devices and obtained at the Institut Guttmann Neurorehabilitation Hospital. © 2010 by the authors.
Martinez-Moreno J.M.,Technical University of Madrid |
Martinez-Moreno J.M.,Biomedical Research Networking Center in Bioengineering |
Sanchez-Gonzalez P.,Technical University of Madrid |
Sanchez-Gonzalez P.,Biomedical Research Networking Center in Bioengineering |
And 8 more authors.
IFMBE Proceedings | Year: 2014
Acquired Brain Injury (ABI) has become one of the most common causes of neurological disability in developed countries. Cognitive disorders result in a loss of independence and therefore patients' quality of life. Cognitive rehabilitation aims to promote patients' skills to achieve their highest degree of personal autonomy. New technologies such as interactive video, whereby real situations of daily living are reproduced within a controlled virtual environment, enable the design of personalized therapies with a high level of generalization and a great ecological validity. This paper presents a graphical tool that allows neuropsychologists to design, modify, and configure interactive video therapeutic activities, through the combination of graphic and natural language. The tool has been validated creating several Activities of Daily Living and a preliminary usability evaluation has been performed showing a good clinical acceptance in the definition of complex interactive video therapies for cognitive rehabilitation. © Springer International Publishing Switzerland 2014.
Marcano-Cedeno A.,Technical University of Madrid |
Chausa P.,Technical University of Madrid |
Garcia A.,Institute Guttmann Neurorehabilitation Hospital |
Caceres C.,Technical University of Madrid |
And 2 more authors.
Expert Systems with Applications | Year: 2013
Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. © 2012 Elsevier Ltd. All rights reserved.