Time-Frequency Analysis of Cardiovascular Variability during an Orthostatic Stress by Complete EMD.

Diverse analysis techniques have been used to comprehend the regulation by the autonomic nervous system (ANS) of the cardiovascular system when a human being faces a stressor. Recently, however, the complete ensemble empirical mode decomposition (EMD) with adaptive noise (CEEMDAN) allows analyzing nonstationary signals in a nonlinear and time- variant way. Consequently, CEEMDAN may provide a means to obtain clues about ANS regulation in health and disease. In this study, we analyze the average Hilbert-Huang spectrum (HHS) of cardiovascular variability signals by CEEMDAN during a head-up tilt test (HUTT) in 12 healthy female subjects and 18 orthostatic intolerance female patients. Beat-to-beat intervals (BBI) as well as systolic (SYS) blood pressure variability time series were analyzed. In addition, instantaneous amplitudes and frequencies of specific intrinsic mode functions (IMF) were investigated separately to define the influence of the disease on ANS regulation. Female groups demonstrated statistical differences in the high-frequency band of BBI but higher differences for the high and low-frequency bands of SYS from the mechanical transition of HUTT.Clinical Relevance- A relevant outcome of the study is the average HHS of healthy female subjects along HUTT. This HHS may be used as reference to help diagnose OI when HHS of the cardiovascular variability signals of any subject deviates from the normal course.

Desarrollo de una Prótesis para Desarticulado de Muñeca Controlada por Señales de Electromiografía / Development of a Myoelectric-Controlled Prosthesis for Transradial Amputees

RESUMEN En este trabajo se presenta el desarrollo y puesta en operación de una prótesis robótica para pacientes amputados con desarticulado de muñeca. Esta prótesis consiste en un prototipo de impresión 3D que tiene dos grados de libertad que permiten realizar tareas de sujeción de tipo pinza, así como la orientación de objetos mediante los movimientos de pronación y supinación. Para el control de la prótesis se utilizan dos clasificadores de manera independiente: un clasificador bayesiano implementado en la plataforma Arduino y una red neuronal artificial implementada en el software MATLAB®; ambos realizan la clasificación de los movimientos mediante la adquisición, procesamiento y extracción de índices característicos de la señal de electromiografía. El clasificador bayesiano y la red neuronal artificial obtuvieron, respectivamente, una eficiencia de 97% y 100%, lo que muestra que los índices característicos seleccionados son adecuados para realizar la clasificación de señales de electromiografía propuesta. Se logró la creación de una prótesis mioeléctrica completamente funcional que, al ser elaborada con tecnología de impresión 3D, representa una alternativa de bajo costo a aquellas ofrecidas actualmente en el mercado.

ABSTRACT In this paper, the development and operation of a robotic prosthesis for transradial amputees is presented. This prosthesis consists in a 3D-printed prototype with two degrees of freedom, allowing the user to perform grip tasks and to orientate objects through pronation and supination movements. Two classifiers were used independently to control the prosthesis: a bayesian classifier implemented in an Arduino device and an artificial neural network implemented in MATLAB® software; both classify movements through the acquisition, processing and extraction of features from the electromyography signal. The bayesian classifier and the artificial neural network achieved an efficiency of 97% and 100%, respectively, which shows that the extracted features were suitable for the proposed electromyography classification. A completely functional 3D-printed myoelectric prosthesis was achieved, and it represents a low-cost alternative to those existent in the current market.

Evaluación de la Presencia de Disautonomía en Pacientes con Parkinson mediante el Análisis de Señales Cardiovasculares / Assessment of the Dysautonomia Presence in Parkinson disease through Cardiovascular Signal Analysis

Resumen: En este trabajo se evalúa y compara la respuesta del sistema nervioso autónomo (SNA) en pacientes con enfermedad de Parkinson (EP) y sujetos sanos para detectar la posible presencia de disautonomía. Las señales de electrocardiograma y fotopletismografía fueron adquiridas durante las maniobras: reposo, cambio de postura (Post-CP), respiración controlada (RC) e hiperventilación (Hip.). El análisis de las señales incluyó índices de la variabilidad de la frecuencia cardiaca (VFC) lineales y no lineales, índices de la señal de tiempo de tránsito de pulso y la sensibilidad del barorreflejo (índice α). Los pacientes con Parkinson mostraron una alteración en la modulación simpática principalmente durante Post-CP y una deficiencia en la respuesta cardiovagal en RC. La entropía aproximada disminuyó significativamente en sujetos sanos respecto a pacientes con EP durante RC. El índice α fue menor en pacientes con EP con respecto a sujetos sanos durante todo el protocolo, lo cual sugiere una alteración en el control del barorreflejo en EP. Sin embargo, es necesario aumentar el número de sujetos con la finalidad de determinar grados de disautonomía. El protocolo diseñado para evaluar la presencia de disautonomía en mexicanos con EP a través de señales no invasivas aportó información sobre el comportamiento del SNA.

Abstract: The goal of this work is to assess and to compare the autonomic nervous system (SNA) response in Parkinson's disease (EP) patients and healthy subjects in order to evaluate the possible dysautonomia presence. Electrocardiogram and photoplethysmography signals were acquired during the following maneuvers: rest, orthostatic change (Post-CP), controlled breathing (RC) and hyperventilation (Hip.). The signal processing was carried out by means of linear and no linear indices of heart rate variability (VFC), indices of pulse transit time (PTT) and baroreflex sensitivity (α index). Parkinson disease patients showed an attenuated sympathetic modulation mainly during Post-CP and the cardiovagal response resulted blunted during RC. Approximate entropy was significantly decreased in healthy subjects with respect to EP subjects during RC. In addition, the index α resulted in lower values in EP patients with respect to healthy subjects during the complete protocol, this result suggests that the baroreflex control in EP patients is blunted. However, is necessary to increase the number of subjects with the objective of determining levels of dysautonomia. The protocol designed to evaluate the dysautonomia presence in mexicans with EP through non invasive signals provides information about the SNA behavior.

Sistema de Apoyo para la Cuantificación Semiautomática de Fibrosis Pulmonar en Imágenes de TAC / Support System for Semiautomatic Quantification of Pulmonary Fibrosis in CT Images

Resumen: En este trabajo se presenta un método para calcular los niveles de fibrosis pulmonar en imágenes de tomografía axial computarizada. Se utilizó un algoritmo de segmentación semiautomática basado en el método de Chan-Vese. El método mostró similitudes de forma cualitativa en la región de la fibrosis con respecto al experto clínico. Sin embargo es necesario validar los resultados con una base de datos mayor. El método propuesto aproxima un porcentaje de fibrosis de forma fácil para apoyar su implementación en la práctica clínica minimizando la subjetividad del experto médico y generando una estimación cuantitativa de la región de fibrosis.

Abstract: A method to estimate the pulmonary fibrosis in computed tomography (CT) imaging is presented. A semi-automatic segmentation algorithm based on the Chan-Vese method was used. The proposed method shows a similar fibrosis región with respect to clinical expert. However, the results need to be validated in a bigger data base. The proposed method approximates a fibrosis percentage that allows to achieve this procedure easily in order to support its implementation in the clinical practice minimizing the clinical expert subjectivity and generating a quantitative estimation of fibrosis region.

Imaging of simulated crackle sounds distribution on the chest.

Crackles sounds have been associated with several pulmonary pathologies and diverse algorithms have been proposed for extracting and counting them from the acquired lung sound. These tasks depend among other factors, of the relation between the magnitude of the crackle and the background lung sound. In this work, we explore multivariate signal processing to deal with the tasks and propose a new concept, the discontinuous adventitious sounds imaging. The image formation is founded on the results of two proposed methodologies that use an autoregressive (AR) model. In the first case, the AR coefficients feed an artificial neural network (ANN) to classify temporal acoustic information as healthy or sick and; in the second case, a time-variant AR (TVAR) model, obtained by the RLS algorithm, permits to detect changes in the TVAR coefficients to be associated with the number of crackles. For AR-ANN, the ratio of the temporal windows classified as sick to the classified as healthy is used as an index to form the adventitious image, while for TVAR-RLS, an estimation of the number of crackles is obtained to form the corresponding image. The results indicated that fine and coarse crackles could be detected and counted even with very low crackle magnitude so that the formation of a crackle distribution image was consistent.

Crackles detection using a time-variant autoregressive model.

Several techniques have been explored to detect automatically fine and coarse crackles; however, the solution for automatic detection of crackles remains insufficient. The purpose of this work was to explore the capacity of the time-variant autoregressive (TVAR) model to detect and to provide an estimate number of fine and coarse crackles in lung sounds. Thus, simulated crackles inserted in normal lung sounds and real lung sounds containing adventitious sounds were processed with TVAR and by an expert that based crackle detection on time-expanded waveform-analysis. The coefficients of the TVAR were obtained by an adaptive filtering prediction scheme. The adaptive filter used the recursive least squares algorithm with a forgetting factor of 0.97 and the model order was four. TVAR model showed an efficiency to detect crackles over 90% even with crackles overlapping and amplitudes as low as 1.5 of the standard deviation of background lung sounds, where expert presented an efficiency around 30%. In conclusion, TVAR model is a proper alternative to detect and to provide an estimate number of fine and coarse crackles, even in presence of crackles overlapping and crackles with low amplitude, conditions where crackles detection based on time-expanded waveform-analysis reveals evident limitations.