Abstract
The Arterial Pressure Waveform (APW) can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1) a custom-designed, non-invasive, electromechanical device was used in the data collection from 50 subjects; (2) the acquired position and amplitude of onset, Systolic Peak (SP), Point of Inflection (Pi) and Dicrotic Wave (DW) were used for the computation of some morphological attributes; (3) pre-processing work on the datasets was performed in order to reduce the number of input features and increase the model accuracy by selecting the most relevant ones; (4) classification of the dataset was carried out using four different machine learning algorithms: Random Forest, BayesNet (probabilistic), J48 (decision tree) and RIPPER (rule-based induction); and (5) we evaluate the trained models, using the majority-voting system, comparatively to the respective calculated Augmentation Index (AIx). Classification algorithms have been proved to be efficient, in particular Random Forest has shown good accuracy (96.95%) and high area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve (0.961). Finally, during validation tests, a correlation between high risk labels, retrieved from the multi-parametric approach, and positive AIx values was verified. This approach gives allowance for designing new hemodynamic morphology vectors and techniques for multiple APW analysis, thus improving the arterial pulse understanding, especially when compared to traditional single-parameter analysis, where the failure in one parameter measurement component, such as Pi, can jeopardize the whole evaluation. © 2013 by the authors; licensee MDPI, Basel, Switzerland.

Abstract
Doppler self-mixing laser probing techniques are often used for vibration measurement with very high accuracy. A novel optoelectronic probe solution is proposed, based on off-the-shelf components, with a direct reflection optical scheme for contactless characterization of the target’s movement. This probe was tested with two test bench apparatus that enhance its precision performance, with a linear actuator at low frequency (35?µm, 5–60?Hz), and its dynamics, with disc shaped transducers for small amplitude and high frequency (0.6?µm, 100–2500?Hz). The results, obtained from well-established signal processing methods for self-mixing Doppler signals, allowed the evaluation of vibration velocity and amplitudes with an average error of less than 10%. The impedance spectrum of piezoelectric (PZ) disc target revealed a maximum of impedance (around 1?kHz) for minimal Doppler shift. A bidimensional scan over the PZ disc surface allowed the categorization of the vibration mode (0,?1) and explained its deflection directions. The feasibility of a laser vibrometer based on self-mixing principles and supported by tailored electronics able to accurately measure submicron displacements was, thus, successfully demonstrated.

Abstract
The pulse pressure waveform has, for long, been known as a fundamental biomedical signal and its analysis is recognized as a non-invasive, simple, and resourceful technique for the assessment of arterial vessels condition observed in several diseases. In the current paper, waveforms from non-invasive optical probe that measures carotid artery distension profiles are compared with the waveforms of the pulse pressure acquired by intra-arterial catheter invasive measurement in the ascending aorta. Measurements were performed in a study population of 16 patients who had undergone cardiac catheterization. The hemodynamic parameters: area under the curve (AUC), the area during systole (AS) and the area during diastole (AD), their ratio (AD/AS) and the ejection time index (ETI), from invasive and non-invasive measurements were compared. The results show that the pressure waveforms obtained by the two methods are similar, with 13% of mean value of the root mean square error (RMSE). Moreover, the correlation coefficient demonstrates the strong correlation. The comparison between the AUCs allows the assessment of the differences between the phases of the cardiac cycle. In the systolic period the waveforms are almost equal, evidencing greatest clinical relevance during this period. Slight differences are found in diastole, probably due to the structural arterial differences. The optical probe has lower variability than the invasive system (13% vs 16%). This study validates the capability of acquiring the arterial pulse waveform with a non-invasive method, using a non-contact optical probe at the carotid site with residual differences from the aortic invasive measurements.

Abstract
A system using laser speckle effect is proposed to segment images reflecting vibration movements of diffuse targets. Longitudinal movements are difficult to identify when simple imaging systems are used. The proposed system produces a two dimensional segmentation of the target and it is sensitive to longitudinal movements. The speckle effect, produced when coherent light is reflected and interferes when hitting rough surfaces, can be used in order to accomplish this purpose. A pattern with high and low intensity spots is observed depending on the illuminated scene. In our optical system, two silicone membranes are illuminated using a beam expanded laser source and their patterns are recorded using a video camera. One of the membranes experiences a longitudinal controlled movement while the remaining scene is still. Speckle data is processed using a temporal gradient and a regional entropy computation. This method produces a binary individual pixel classification. Four sets of parameters have been tested for the entropy computation and the area under the receiver operating characteristic (ROC) curve was used to select the best one. The selected set-up achieved a ROC value of 0.9879. A data set with 12 different membrane velocities was used to define the threshold that maximizes the classifier accuracy. This threshold was applied to a validation data-set composed by 4 sinusoidal movements with distinct velocities. The accuracy of this technique has achieved values between 92% and 97%. The results show that the target was accurately identified with the optical non-contact apparatus and the developed algorithm.

Abstract
The great incidence of cardiovascular (CV) diseases in the world spurs the search for new solutions to enable an early detection of pathological processes and provides more precise diagnosis based in multi-parameters assessment. The pulse wave velocity (PWV) is considered one of the most important clinical parameters for evaluate the CV risk, vascular adaptation, and therapeutic efficacy. Several studies were dedicated to find the relationship between PWV measurement and pathological status in different diseases, and proved the relevance of this parameter. The commercial devices dedicate to PWV estimation make a regional assessment (measured between two vessels), however a local measurement is more precise evaluation of artery condition, taking into account the differences in the structure of arteries. Moreover, the current devices present some limitations due to the contact nature. Emerging trends in CV monitoring are moving away from more invasive technologies to non-invasive and non-contact solutions. The great challenge is to explore the new instrumental solutions that allow the PWV assessment with fewer approximations for an accurately evaluation and relatively inexpensive techniques in order to be used in the clinical routine.

Abstract