Abstract
Cardiovascular diseases (CVDs) are the leading cause of death in the world. The pulse wave analysis provides a new insight in the analysis of these pathologies, while data mining techniques can contribute for an efficient diagnostic method. Amongst the various available techniques, artificial neural networks (ANNs) are well established in biomedical applications and have numerous successful classification applications. Also, clustering procedures have proven to be very useful in assessing different risk groups in terms of cardiovascular function in healthy populations. In this paper, a robust data mining approach was performed for cardiac risk patterns identification. Eight classifiers were tested: C4.5, Random Forest, RIPPER, Naive Bayes, Bayesian Network, Multy-layer perceptron (MLP) (1 and 2-hidden layers) and radial basis function (RBF). As for clustering procedures, k-means clustering (using Euclidean distance) and expectation-maximization (EM) were the chosen algorithms. Two datasets were used as case studies to perform classification and clustering analysis. The accuracy values are good with intervals between 88.05% and 97.15%. The clustering techniques were essential in the analysis of a dataset where little information was available, allowing the identification of different clusters that represent different risk group in terms cardiovascular function. The three cluster analysis has allowed the characterization of distinctive features for each of the clusters. Reflected wave time (T_RP) and systolic wave time (T_SP) were the selected features for clusters visualization. Data mining methodologies have proven their usefulness in screening studies due to its descriptive and predictive power.

Abstract
There is good evidence that naltrexone, an opioid antagonist, has a strong neuroprotective role and may be a potential drug for the treatment of fibromyalgia. In previous work, some of the authors used experimental clinical data to identify input-output linear time invariant models that were used to extract useful information about the effect of this drug on fibromyalgia symptoms. Additional factors such as anxiety, stress, mood, and headache, were considered as additive disturbances. However, it seems reasonable to think that these factors do not affect the drug actuation, but only the way in which a participant perceives how the drug actuates on herself. Under this hypothesis the linear time invariant models can be replaced by State-Space Affine Linear Parameter Varying models where the disturbances are seen as a scheduling signal signal only acting at the parameters of the output equation. In this paper a new algorithm for identifying such a model is proposed. This algorithm minimizes a quadratic criterion of the output error. Since the output error is a linear function of some parameters, the Affine Linear Parameter Varying system identification is formulated as a separable nonlinear least squares problem. Likewise other identification algorithms using gradient optimization methods several parameter derivatives are dynamical systems that must be simulated. In order to increase time efficiency a canonical parametrization that minimizes the number of systems to be simulated is chosen. The effectiveness of the algorithm is assessed in a case study where an Affine Parameter Varying Model is identified from the experimental data used in the previous study and compared with the time-invariant model.

Abstract
An indirect downsampling approach for continuous-time input/output system identification is proposed. This modus operandi was introduced to system identification through a sub-space algorithm, where the input/output data set is partitioned into lower rate m subsets. Then, a state-space discrete-time model is identified by fusing the data subsets into a single one. In the present work the identification of the input/output downsampled model is performed by a least squares and a simplified refined instrumental variables (IV) procedures. In this approach, the inter-sample behaviour is preserved by the addition of fictitious inputs, leading to an increase of excitation requirements of the input signal. This over requirement is removed by directly estimating from the data the parameters of the transfer function numerator. The performance of the method is illustrated using the Rao-Garnier test system.

Abstract
Localized neurodevelopmental defects provide an opportunity to study structure-function correlations in the human nervous system. This unique multimodal case report of epileptogenic dysplasia in the visual cortex allowed exploring visual function across distinct pathways in retinotopic regions and the dorsal stream, in relation to fMRI retinotopic mapping and spike triggered BOLD responses. Pre-surgical EEG/video monitoring, MRI/DTI, EEG/fMRI, PET and SPECT were performed to characterize structure/function correlations in this patient with a very early lesion onset. In addition, we included psychophysical methods (assessing parvo/konio and magnocellular pathways) and retinotopic mapping. We could identify dorsal stream impairment (with extended contrast sensitivity deficits within the input magno system contrasting with more confined parvocellular deficits) with disrupted active visual field input representations in regions neighboring the lesion. Simultaneous EEG/fMRI identified perilesional and retinotopic bilaterally symmetric BOLD deactivation triggered by interictal spikes, which matched the contralateral spread of magnocellular dysfunction revealed in the psychophysical tests. Topographic changes in retinotopic organization further suggested long term functional effects of abnormal electrical discharges during brain development. We conclude that fMRI based visual field cortical mapping shows evidence for retinotopic dissociation between magno and parvocellular function well beyond striate cortex, identifiable in high level dorsal visual representations around visual area V3A which is consistent with the effects of epileptic spike triggered negative BOLD.

Abstract
New optical probes are developed for carotid distention waveform measurements, in order to assess the risk of cardiovascular diseases. The probes make use of two distinct photodetectors: planar and avalanche photodiodes. Their performance is compared for visible and infrared (IR) light wavelengths. The test setup designed for the evaluation of the probes simulates the fatty deposits commonly seen in the obese people, between skin and the artery. The performed tests show that the attenuation of the signal is lower for the IR light, with higher penetration and better resolution in the captured distension waveform, with higher definition in morphological features on the wave and higher signal-to-noise ratio when compared to the visible source signals. The probes show good overall performance in the test setup with a root mean square error lower than 8%. In vivo, the IR probes allow easier waveform detection, even more relevant with the increasing deposit structures.

Abstract
Assistive Technologies may greatly contribute to give autonomy and independence for individuals with physical limitations. Electric wheelchairs are examples of those assistive technologies and nowadays each time becoming more intelligent due to the use of technology that provides assisted safer driving. Usually, the user controls the electric wheelchair with a conventional analog joystick. However, this implies the need for an appropriate methodology to map the position of the joystick handle, in a Cartesian coordinate system, to the wheelchair wheels intended velocities. This mapping is very important since it will determine the response behavior of the wheelchair to the user manual control. This paper describes the implementation of several joystick mappings in an intelligent wheelchair (IW) prototype. Experiments were performed in a realistic simulator using cerebral palsy users with distinct driving abilities. The users had 6 different joystick control mapping methods and for each user the usability and the users' preference order was measured. The results achieved show that a linear mapping, with appropriate parameters, between the joystick's coordinates and the wheelchair wheel speeds is preferred by the majority of the users.