31 research outputs found

    Optical Gas Sensing: Media, Mechanisms and Applications

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    Optical gas sensing is one of the fastest developing research areas in laser spectroscopy. Continuous development of new coherent light sources operating especially in the Mid-IR spectral band (QCL—Quantum Cascade Lasers, ICL—Interband Cascade Lasers, OPO—Optical Parametric Oscillator, DFG—Difference Frequency Generation, optical frequency combs, etc.) stimulates new, sophisticated methods and technological solutions in this area. The development of clever techniques in gas detection based on new mechanisms of sensing (photoacoustic, photothermal, dispersion, etc.) supported by advanced applied electronics and huge progress in signal processing allows us to introduce more sensitive, broader-band and miniaturized optical sensors. Additionally, the substantial development of fast and sensitive photodetectors in MIR and FIR is of great support to progress in gas sensing. Recent material and technological progress in the development of hollow-core optical fibers allowing low-loss transmission of light in both Near- and Mid-IR has opened a new route for obtaining the low-volume, long optical paths that are so strongly required in laser-based gas sensors, leading to the development of a novel branch of laser-based gas detectors. This Special Issue summarizes the most recent progress in the development of optical sensors utilizing novel materials and laser-based gas sensing techniques

    Smart Sensors for Healthcare and Medical Applications

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    This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

    Influence of Early Bilingual Exposure in the Developing Human Brain.

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    190 p.La adquisición del lenguaje es un proceso que ese encuentra determinado tanto por mecanismos de desarrollo cognitivo, como por la experiencia lingüística durante los primeros años de vida. Aunque se trata de un proceso relativamente complejo, los bebés muestran una gran habilidad para el aprendizaje del lenguaje. Un entorno de aprendizaje lingüístico bilingüe podría considerarse aun más complejo, ya que los bebés están expuestos a las características lingüísticas de dos lenguas simultáneamente. En primer lugar, los bebés que crecen en un entorno bilingüe tienen que ser capaces de darse cuenta de que están expuestos a dos lenguas diferentes, y posteriormente deben separar y aprender las características especificas de cada una de ellas; por ejemplo, los distintos fonemas, palabras o estructuras gramaticales. Aunque la exposición lingüística total de los bebés bilingües debería ser comparable a la de los bebés monolingües, es probable que la exposición a cada una de las lenguas de su entorno sea menor, ya que tienen que dividir su tiempo de exposición entre ambas. Si bien los bebés bilingües parecen no tener problemas para enfrentarse a un contexto de aprendizaje potencialmente más complejo, ya que alcanzan las distintas etapas de adquisición del lenguaje a un ritmo similar a los bebés monolingües, sí se han observado adaptaciones a nivel conductual y a nivel de funcionamiento cerebral que podrían producirse como consecuencia de este contexto.Basque Center on cognition, brain and languag

    Development and application of novel processing tools and methods for cardiac optical mapping

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    Cardiac optical mapping provides unparalleled spatio-temporal resolution information of cardiac electrophysiology. It has hence emerged as an important technology in understanding cardiac electrical behaviour in physiological and pathophysiological states. There is a requirement for effective data analysis tools that are high-throughput, robustly characterised and flexible with regards to a growing array of experimental models. In this thesis a MATLAB based software, ElectroMap, was developed for analysis of diverse optical mapping datasets. ElectroMap incorporates existing and novel methods to allow quantification and mapping of action potential and calcium transient morphology and activation/repolarisation times. Automated pacing cycle length detection and segmentation were implemented, realising high-throughput analysis of beat-to-beat responses and transient behaviour. Standalone modules dedicated to calculation of conduction velocity and alternans were introduced, allowing thorough integration of key factors in arrhythmogenesis. Semi-automated analysis of temporal variations in wave morphology were developed from previous methodologies for electrogram analysis. Algorithms to use fractional rate of change of fluorescence as a measure of conduction were also introduced to the software. Algorithms were tested in silico datasets, mouse and guinea pig optical mapping datasets and preliminary experiments also showed use for in vivo human electrogram mapping of atrial fibrillation

    Methods and instrumentation for raman characterization of bladder cancer tumor

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    High incidence and recurrence rates make bladder cancer the most common malignant tumor in the urinary system. Cystoscopy is the gold standard test used for diagnosis, nevertheless small flat tumors might be missed, and the procedure still represents discomfort to patients and high recurrence can result from of urethral injuries. During cystoscopy, suspicious tumors are detected through white light endoscopy and resected tissue is further examined by histopathology. after resection, the pathologist provides information on the differentiation of the cells and the penetration depth of the tumor in the tissue, known as grading and staging of tumor, respectively. During cystoscopy, information on tumor grading and morphological depth characterization can assist onsite diagnosis and significantly reduce the amount of unnecessarily resected tissue. Recently, new developments in optical imaging and spectroscopic approaches have been demonstrated to improve the results of standard techniques by providing real-time detection of macroscopic and microscopic biomedical information. Different applications to detect anomalies in tissues and cells based on the chemical composition and structure at the microscopic level have been successfully tested. There is, nevertheless, the need to cope with the demands for clinical translation. This doctoral thesis presents the investigations, clinical studies and approaches applied to filling the main open research questions when applying Raman spectroscopy as a diagnostic tool for bladder cancer tumor grading and general Raman spectroscopy-based oncological clinical studies

    Separating Signal from Noise in High-Density Diffuse Optical Tomography

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    High-density diffuse optical tomography (HD-DOT) is a relatively new neuroimaging technique that detects the changes in hemoglobin concentrations following neuronal activity through the measurement of near-infrared light intensities. Thus, it has the potential to be a surrogate for functional MRI (fMRI) as a more naturalistic, portable, and cost-effective neuroimaging system. As in other neuroimaging modalities, head motion is the most common source of noise in HD-DOT data that results in spurious effects in the functional brain images. Unlike other neuroimaging modalities, data quality assessment methods are still underdeveloped for HD-DOT. Therefore, developing robust motion detection and motion removal methods in its data processing pipeline is a crucial step for making HD-DOT a reliable neuroimaging modality. In particular, our lab is interested in using HD-DOT to study the brain function in clinical populations with metal implants that cannot be studied using fMRI due to their contraindications. Two of these populations are patients having movement disorders (Parkinson Disease or essential tremor) with deep brain stimulation (DBS) implants and individuals with cochlear implants (CI). These two groups both receive tremendous benefit from their implants at the statistical level; however, there is significant single-subject variability. Our overarching goal is to use HD-DOT to find the relationships between the neuronal function and the behavioral measures in these populations to optimize the contact location of these implant surgeries. However, one of the challenges in analyzing the data in these subjects, especially in patients with DBS, is their high levels of motion due to tremors when their DBS implant is turned off. This further motivates the importance of the methods presented herein for separating signal from noise in HD-DOT data. To this end, I will first assess the efficacy of state-of-the-art motion correction methods introduced in the fNIRS literature for HD-DOT. Then, I will present a novel global metric inspired by motion detection methods in fMRI called GVTD (global variance of the temporal derivatives). Our results show that GVTD-based motion detection not only outperforms other comparable motion detection methods in fNIRS, but also outperforms motion detection with accelerometers. I will then present my work on collecting and processing HD-DOT data for two clinical populations with metal implants in their brain and the preliminary results for these studies. Our results in PD patients show that HD-DOT can reliably map neuronal activity in this group and replicate previously published results using PET and fMRI. Our results in the CI users provide evidence for the recruitment of the prefrontal cortex in processing speech to compensate for the decreased activity in the temporal cortex. These findings support the theory of cognitive demand increase in effortful listening situations. In summary, the presented methods for separating signal from noise enable direct comparisons of HD-DOT images with those of fMRI in clinical populations with metal implants and equip this modality to be used as a surrogate for fMRI

    Classification before regression for improving the accuracy of glucose quantification using absorption spectroscopy

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    This work contributes to the improvement of glucose quantification using near-infrared (NIR), mid-infrared (MIR), and combination of NIR and MIR absorbance spectroscopy by classifying the spectral data prior to the application of regression models. Both manual and automated classification are presented based on three homogeneous classes defined following the clinical definition of the glycaemic ranges (hypoglycaemia, euglycaemia, and hyperglycaemia). For the manual classification, partial least squares and principal component regressions are applied to each class separately and shown to lead to improved quantification results compared to when applying the same regression models for the whole dataset. For the automatic classification, linear discriminant analysis coupled with principal component analysis is deployed, and regressions are applied to each class separately. The results obtained are shown to outperform those of regressions for the entire dataset

    Clinical Decision Support Systems with Game-based Environments, Monitoring Symptoms of Parkinson’s Disease with Exergames

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    Parkinson’s Disease (PD) is a malady caused by progressive neuronal degeneration, deriving in several physical and cognitive symptoms that worsen with time. Like many other chronic diseases, it requires constant monitoring to perform medication and therapeutic adjustments. This is due to the significant variability in PD symptomatology and progress between patients. At the moment, this monitoring requires substantial participation from caregivers and numerous clinic visits. Personal diaries and questionnaires are used as data sources for medication and therapeutic adjustments. The subjectivity in these data sources leads to suboptimal clinical decisions. Therefore, more objective data sources are required to better monitor the progress of individual PD patients. A potential contribution towards more objective monitoring of PD is clinical decision support systems. These systems employ sensors and classification techniques to provide caregivers with objective information for their decision-making. This leads to more objective assessments of patient improvement or deterioration, resulting in better adjusted medication and therapeutic plans. Hereby, the need to encourage patients to actively and regularly provide data for remote monitoring remains a significant challenge. To address this challenge, the goal of this thesis is to combine clinical decision support systems with game-based environments. More specifically, serious games in the form of exergames, active video games that involve physical exercise, shall be used to deliver objective data for PD monitoring and therapy. Exergames increase engagement while combining physical and cognitive tasks. This combination, known as dual-tasking, has been proven to improve rehabilitation outcomes in PD: recent randomized clinical trials on exergame-based rehabilitation in PD show improvements in clinical outcomes that are equal or superior to those of traditional rehabilitation. In this thesis, we present an exergame-based clinical decision support system model to monitor symptoms of PD. This model provides both objective information on PD symptoms and an engaging environment for the patients. The model is elaborated, prototypically implemented and validated in the context of two of the most prominent symptoms of PD: (1) balance and gait, as well as (2) hand tremor and slowness of movement (bradykinesia). While balance and gait affections increase the risk of falling, hand tremors and bradykinesia affect hand dexterity. We employ Wii Balance Boards and Leap Motion sensors, and digitalize aspects of current clinical standards used to assess PD symptoms. In addition, we present two dual-tasking exergames: PDDanceCity for balance and gait, and PDPuzzleTable for tremor and bradykinesia. We evaluate the capability of our system for assessing the risk of falling and the severity of tremor in comparison with clinical standards. We also explore the statistical significance and effect size of the data we collect from PD patients and healthy controls. We demonstrate that the presented approach can predict an increased risk of falling and estimate tremor severity. Also, the target population shows a good acceptance of PDDanceCity and PDPuzzleTable. In summary, our results indicate a clear feasibility to implement this system for PD. Nevertheless, long-term randomized clinical trials are required to evaluate the potential of PDDanceCity and PDPuzzleTable for physical and cognitive rehabilitation effects

    Influence of Early Bilingual Exposure in the Developing Human Brain.

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    190 p.La adquisición del lenguaje es un proceso que ese encuentra determinado tanto por mecanismos de desarrollo cognitivo, como por la experiencia lingüística durante los primeros años de vida. Aunque se trata de un proceso relativamente complejo, los bebés muestran una gran habilidad para el aprendizaje del lenguaje. Un entorno de aprendizaje lingüístico bilingüe podría considerarse aun más complejo, ya que los bebés están expuestos a las características lingüísticas de dos lenguas simultáneamente. En primer lugar, los bebés que crecen en un entorno bilingüe tienen que ser capaces de darse cuenta de que están expuestos a dos lenguas diferentes, y posteriormente deben separar y aprender las características especificas de cada una de ellas; por ejemplo, los distintos fonemas, palabras o estructuras gramaticales. Aunque la exposición lingüística total de los bebés bilingües debería ser comparable a la de los bebés monolingües, es probable que la exposición a cada una de las lenguas de su entorno sea menor, ya que tienen que dividir su tiempo de exposición entre ambas. Si bien los bebés bilingües parecen no tener problemas para enfrentarse a un contexto de aprendizaje potencialmente más complejo, ya que alcanzan las distintas etapas de adquisición del lenguaje a un ritmo similar a los bebés monolingües, sí se han observado adaptaciones a nivel conductual y a nivel de funcionamiento cerebral que podrían producirse como consecuencia de este contexto.Basque Center on cognition, brain and languag
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