Publications

In the present work, we have studied the QRS loop in the Vectorcardiogram (VCG) of 95 chronic chagasic patients classified in different groups (I, II and III) according to their degree of myocardial damage. For comparison, the VCGs of 11 healthy subjects used as control group (Group O) were also examined. The QRS loop was obtained for each patient from the XYZ orthogonal leads of their High-Resolution Electrocardiogram (HRECG) records. In order to analyze the variations of QRS loop in each detected beat, it has been proposed in this study the following vectorcardiographic parameters a) Maximum magnitude of the cardiac depolarization vector, b) Volume, c) Area of QRS loop, d) Ratio between the Area and Perimeter, e) Ratio between the major and minor axes of the QRS loop and f) QRS loop Energy. It has been found that one or more indexes exhibited statistical differences (p<0.05) between groups 0-II, O-III, I-II, I-III and II-III. We concluded that the proposed method could be use as complementary diagnosis technique to evaluate the degree of myocardial damage in chronic chagasic patients.

JTD Keywords: Practical, Experimental/ bioelectric phenomena, Diseases, Electrocardiography, Medical signal, Processing/ QRS loop, Vectorcardiogram, Cardiac depolarization vector, Myocardial damage, Chagas disease, Complementary diagnosis technique, High-resolution electrocardiogram

The differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events but its invasiveness deters its usage in clinical routine. Flattening patterns appear in the airflow signal during episodes of inspiratory flow limitation (IFL) and have been shown with invasive techniques to be useful to differentiate between central and obstructive hypopneas. In this study we present a new method for the automatic non-invasive differentiation of obstructive and central hypopneas solely with nasal airflow. An overall of 36 patients underwent full night polysomnography with systematic Pes recording and a total of 1069 hypopneas were manually scored by human experts to create a gold-standard annotation set. Features were automatically extracted from the nasal airflow signal to train and test our automatic classifier (Discriminant Analysis). Flattening patterns were non-invasively assessed in the airflow signal using spectral and time analysis. The automatic non-invasive classifier obtained a sensitivity of 0.71 and an accuracy of 0.69, similar to the results obtained with a manual non-invasive classification algorithm. Hence, flattening airflow patterns seem promising for the non-invasive differentiation of obstructive and central hypopneas.

JTD Keywords: Practical, Experimental/ biomedical measurement, Feature extraction, Flow measurement, Medical disorders, Medical signal processing, Patient diagnosis, Pneumodynamics, Pressure measurement, Signal classification, Sleep, Spectral analysis/ automatic noninvasive differentiation, Obstructive hypopnea, Central hypopnea, Inspiratory flow limitation, Nasal airflow, Esophageal pressure, Polysomnography, Feature extraction, Discriminant analysis, Spectral analysis

In this study we propose the correntropy function as a discriminative measure for detecting nonlinearities in the respiratory pattern of chronic heart failure (CHF) patients with periodic or nonperiodic breathing pattern (PB or nPB, respectively). The complexity seems to be reduced in CHF patients with higher risk level. Correntropy reflects information on both, statistical distribution and temporal structure of the underlying dataset. It is a suitable measure due to its capability to preserve nonlinear information. The null hypothesis considered is that the analyzed data is generated by a Gaussian linear stochastic process. Correntropy is used in a statistical test to reject the null hypothesis through surrogate data methods. Various parameters, derived from the correntropy and correntropy spectral density (CSD) to characterize the respiratory pattern, presented no significant differences when extracted from the iteratively refined amplitude adjusted Fourier transform (IAAFT) surrogate data. The ratio between the powers in the modulation and respiratory frequency bands R was significantly different in nPB patients, but not in PB patients, which reflects a higher presence of nonlinearities in nPB patients than in PB patients.

JTD Keywords: Practical, Theoretical or Mathematical, Experimental/cardiology diseases, Fourier transforms, Medical signal processing, Pattern classification, Pneumodynamics, Spectral analysis, Statistical analysis, Stochastic processes/ correntropy based nonlinearity test, Chronic heart failure, Correntropy function, Respiratory pattern nonlinearities, CHF patients, Nonperiodic breathing pattern, Dataset statistical distribution, Dataset temporal structure, Nonlinear information, Null hypothesis, Gaussian linear stochastic process, Statistical test, Correntropy spectral density, Iteratively refined amplitude adjusted Fourier transform, Surrogate data, Periodic breathing pattern

A new method for the quantification of amplitude variations in biomedical signals through moving approximate entropy is presented. Unlike the usual method to calculate the approximate entropy (ApEn), in which the tolerance value (r) varies based on the standard deviation of each moving window, in this work ApEn has been computed using a fixed value of r. We called this method, moving approximate entropy with fixed tolerance values: ApEn/sub f/. The obtained results indicate that ApEn/sub f/ allows determining amplitude variations in biomedical data series. These amplitude variations are better determined when intermediate values of tolerance are used. The study performed in diaphragmatic mechanomyographic signals shows that the ApEn/sub f/ curve is more correlated with the respiratory effort than the standard RMS amplitude parameter. Furthermore, it has been observed that the ApEn/sub f/ parameter is less affected by the existence of impulsive, sinusoidal, constant and Gaussian noises in comparison with the RMS amplitude parameter.

JTD Keywords: Practical, Theoretical or Mathematical/ biomechanics, Entropy, Gaussian noise, Medical signal processing, Muscle, Random processes/ approximate entropy interpretation, Fixed tolerance values, Diaphragmatic mechanomyographic signals, ApEnf curve, Respiratory effort, Gaussian noises

Sleep science and respiratory engineering as medical subspecialties and research areas grew up side-by-side with biomedical engineering. The formation of EMBS in the 1950's and the discovery of REM sleep in the 1950's led to parallel development and interaction of sleep and biomedical engineering in diagnostics and therapeutics.

JTD Keywords: Practical/ biomedical equipment, Biomedical measurement, Patient diagnosis, Patient monitoring, Patient treatment, Pneumodynamics, Sleep/ sleep engineering, Respiratory engineering, Automatic sleep analysis, Automatic sleep interpretation systems, Breathing, Biomedical, Engineering, Diagnostics, Therapeutics, REM sleep, Portable, Measurement, Ambulatory measurement, Monitoring

The design of orthotic devices that tries to fit to the anthropomorphic structure of human limbs faces the problem of achieving the highest approximation to the anatomical kinematics. This paper studies the main characteristics and performances of orthotic devices, mainly focusing on the upper limbs, and proposes a solution to the problem of the superposition of rotation and displacement of some joints, as the shoulder, elbow or knee. A 3 DoF virtual joint is proposed to emulate a human joint, solving the isocentricity and size adaptation of most current orthosis.

JTD Keywords: Prosthetics and other practical applications, Prosthetics and orthotics, Prosthetic and orthotic control systems, Robotics, Biomechanics (mechanical engineering), Robot and manipulator mechanics

Sleep Apnea-Hypopnea Syndrome (SAHS) diagnosis is still done with an overnight multi-channel polysomnography. Several efforts are being made to study profoundly the snore mechanism and discover how it can provide an opportunity to diagnose the disease. This work introduces the concept of regular snores, defined as the ones produced in consecutive respiratory cycles, since they are produced in a regular way, without interruptions. We applied 2 thresholds (TH/sub adaptive/ and TH/sub median/) to the time interval between successive snores of 34 subjects in order to select regular snores from the whole all-night snore sequence. Afterwards, we studied the effectiveness that parameters, such as time interval between successive snores and the mean intensity of snores, have on distinguishing between different levels of SAHS severity (AHI (Apnea-Hypopnea Index)<5h/sup -1/, AHI<10 h/sup -1/, AHI<15h/sup -1/, AHI<30h/sup -1/). Results showed that TH/sub adaptive/ outperformed TH/sub median/ on selecting regular snores. Moreover, the outcome achieved with non-regular snores intensity features suggests that these carry key information on SAHS severity.

JTD Keywords: Practical, Experimental/ acoustic signal processing, Bioacoustics, Biomedical measurement, Diseases, Feature extraction, Medical signal processing, Patient diagnosis, Pneumodynamics, Sleep/ nonregular snore features, SAHS markers, Sleep apnea hypopnea syndrome, Overnight multichannel polysomnography, Snore mechanism