Abstract
Blood pressure (BP) determination is a fundamental parameter in cardiovascular assessment. The gold standard method to measure BP is based on the inflatable arm cuff, however has several disadvantages for continuous monitoring. New techniques were developed to overcome these limitations using correlations between the pulse transit time (PTT) and BP. This work draws attention to the PTT rationale using several methods. In order to determine the PTT, an electrocardiogram (ECG) was used combined with multiple photoplethysmography (PPG) sensors applied to different arm locations, these signals were acquired with a bioPLUX device. The Ultrassound system (SonoSite Edge) was used to measure the artery diameter. As reference, BP was measured using a cuff- based sphygmomanometric device. Measurements were performed in a study population of 36 volunteers. The correlation coefficient for DBP determined and DBP measured was r = 0,689. The results suggest PTT deduced from different locations can be used to measure BP.

Abstract

Abstract

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.