Abstract
Local pulse-wave velocity (PWV) is recognized as the simplest and most reproducible process of non-invasively assessing the vascular marker of arterial stiffness that allowing the risk of cardiovascular diseases to be determinate. Devices currently available for local PWV measurement have not yet been generalized to clinical practice since they require high technical expertise and most of them are limited in precision, due to the lack of reliable signal processing methods. This work describes a new type of probes, based on a piezoelectric sensor in different configurations, single probe and double probe. The principle of PWV measurement involves determination of the pulse transit time between the signals acquired simultaneously by both piezoelectric placed 23 mm apart in the same probe. The double probe characterization is accomplished in different studies, carried out in a dedicated test bench system, capable of reproducing a range of clinically relevant properties of the cardiovascular system.

Abstract
This paper envisages showing the potential of innovative non-invasive techniques based on affordable and easily operated instrumentation as well as user-friendly computer aided algorithms in the screening of cardiovascular (CV) diseases. These techniques are based on the assumption that arterial stiffness is currently an important predicator of the CV diseases development and can be assessed by analyzing the arterial pressure waveform (APW). A previously developed PZ based device for non-invasive APW capture is currently under test in clinical environment, using a heterogeneous population constituted by healthy and unhealthy subjects. A dedicated Matlab analysis tool was designed and developed to extract relevant information and further APW analysis. Several recordings of the APW in the same day and in consecutive months are being performed by trained observers, to evaluate its reproducibility. Data mining analysis is subsequently the last task where the Weka 3-6-5 package software is used. The usefulness of developing data mining algorithms for cardiovascular applications can benefit the CV screenings contributing for the early identification of arterial stiffness related patterns.

Abstract
We address the identification of discrete-time linear parameter varying systems in the state-space form with affine parameter dependence. In previous work, some of the authors have addressed this problem and an iterative algorithm that avoids the curse of dimensionality, inherent to this class of problems, was developed for the identification of multiple input multiple output systems. Although convergence of this algorithm has been assured for white noise sequences, it has also converged for other type of scheduling signals. Never less, its application is still not generalized to every class of scheduling parameters. In this paper, the algorithm is modified in order to identify multiple input single output systems with quasi-stationary scheduling signals. In every iteration, the system is modeled as a linear time invariant system driven by an extended input composed by the measured input, the Kronecker product between this signal and the scheduling parameter and the Kronecker product between the scheduling and the state estimated at the previous iteration. The remaining unknown signals are considered as "noise". Furthermore, the system is decomposed into a "deterministic" system driven by the known inputs and a "stochastic" subsystem driven by noise. The system is identified as a high order autoregressive exogeneous model. In order to whiten the noise, the input/output data is filtered by the inverse noise transfer function and a state-space model is estimated for the "deterministic" subsystem. Then, the output simulated by this system is subtracted from the measurements to obtain the output stochastic component. Finally, the state of the system is estimated using a Kalman filter and a deconvolution technique. Then, the state becomes an entry to the system for the next iteration, after being multiplied by the scheduling parameter. The whole process is repeated until convergence. The algorithm is tested using periodic scheduling signals and compared with other approaches developed by the same authors. © 2012 IFAC.

Abstract
This paper addresses the detection and classification of low amplitude signals within the QRS complex of the signal-averaged electrocardiogram. Linear and bilinear Kalman filter models are fitted using the subspace system identification family of algorithms. If the residuals from the models are a white noise process, then anything that cannot be modeled with the state-space models will show up in the residuals as low amplitude signal + noise. Diagnostic tests and analysis on the residuals will then lead to detection and classification of abnormalities in the intra-QRS complex. The end result is a diagnostic tool to aid the physician. © 2012 IFAC.

Abstract
In this paper the Wiener-Hammerstein Benchmark is identified as a bilinear discrete system. The bilinear approximation relies on both facts that the Wiener-Hammerstein system can be described by a Volterra series which can be approximated by bilinear systems. The identification is performed with an iterative bilinear subspace identification algorithm previously proposed by the authors. In order to increase accuracy, polynomial static nonlinearities were added to the bilinear model input. These Hammerstein type bilinear models are then identified using the same iterative subspace identification algorithm.

Abstract
Introduction: Current scientific knowledge reinforces that successful reorganization of surviving nervous tissue supports cognitive and motor recovery after stroke. The development of new neurorehabilitation tools to modulate this physiologic process is needed. In this context, vibratory stimuli are a noninvasive form of proprioceptive stimulation of the nervous system and are freely available and easy to use at a low cost. Objectives: To access the feasibility and tolerability of targeted vibratory stimuli delivered through a wearable device in an early post-stroke setting. Patients and methods: Five stroke patients were recruited from a stroke unit setting having a first ever medial cerebral artery ischemic stroke with motor deficit. The stimulation device developed delivered external vibratory stimuli to major joints at preprogrammed arrays of intensity, duration and interval of actuation. The tolerability test was set for five-hour duration and during that period data on vital parameters, cognitive, motor and sensitive performance as well as anxiety scores were recorded. Results: The device remained in place throughout and none of the patients or relatives asked to interrupt the tolerability test. There were no major complications during the trial or the ensuing days. Attention to the affected side during stimulation was increased in four patients, and two were reported as clearly more awake during the test. Discussion: This is the first tolerability test focused on the use of targeted vibratory stimulus as a neurorehabilitation tool in stroke patients. There were no hazards to report and most interestingly the majority of patients showed increased awareness to the affected side of the body. These findings will be further analyzed under functional MRI control and on long-term ambulatory use trials.