RESUMO

Este artículo presenta un método no obstructivo para la detección del síndrome de apnea-hipopnea del sueño (SAHS). El flujo respiratorio es medido indirectamente a través de un colchón sensorizado (PBS Pressure Bed Sensor) que incluye 8 transductores de presión. Mediante la transformada de Hilbert se obtiene la amplitud instantánea de las señales respiratorias y se reduce la información a través del análisis de componentes principales (ACP). Los eventos respiratorios (ERs apneas/hipopneas) se localizan como una reducción en la amplitud instantánea resultante y se contabilizan en el índice de eventos respiratorios (IER), un índice de severidad similar al oficial apnea-hypopnea index (AHI). El PBS se analiza agrupando primero la información de pares de canales y después utilizando los 8 canales. Los IER se evalúan comparándolos con el AHI en diferentes niveles de severidad. En el diagnóstico de pacientes sanos y patológicos se obtuvo una sensibilidad, especificidad y exactitud de 92%, 100% y 96% respectivamente, utilizando la información de dos u ocho canales. Con estos resultados podemos proponer el uso del PBS como una alternativa para el diagnóstico del SAHS en ambientes fuera del hospital, ya que no requiere la presencia de un clínico especialista para su uso.

This manuscript presents an unobtrusive method for sleep apneahypopnea syndrome (SAHS) detection. The airflow is indirectly measured through a sensitive mattress (Pressure Bed sensor, PBS) that incorporates multiple pressure sensors into a bed mattress. The instantaneous amplitude of each sensor signal is calculated through Hilbert transform, and then, the information is reduced via principal component analysis. The respiratory events (ERs -apneas/hypopneas) are detected as a reduction in the resulting instantaneous amplitude and accounted in the respiratory event index (IER), which is a severity indicator similar to the offcial apnea-hypopnea index (AHI). The respiratory signals extracted from PBS are analyzed first by clustering the information coming from channel pairs, and then using the eight channels. The IER performance is compared with the AHI for different severity categories. For the diagnosis of healthy and pathological patients we obtain a sensitivity, specificity and accuracy of 92%, 100% and 96%, respectively using two or eight PBS channels. These results suggest the possibility to propose PBS as an alternative tool for SAHS diagnosis in home environment.

RESUMO

Multi-Spectral Fluorescent Lifetime Imaging Microscopy (m-FLIM) is a technique that aims to perform noninvasive in situ clinical diagnosis of several diseases. It measures the endogenous fluorescence of molecules, recording their lifetime decay in different wavelength bands. This signal is a mixed response of multiple fluorescent components present in a tissue sample. The goal is to decompose the mixture and estimate the proportional contributions of its constituents. Estimation of such quantitative description will help to characterize the molecular constitution of a given sample. This paper presents a new method to estimate the abundances of multiple components present in a mixture measured using m-FLIM data. It provides a closed-form solution under the fully constrained linear unmixing model and assuming the number of components as well as their ideal lifetime decays are known. Its performance is tested using synthetic samples with three components, where performance can be measured accurately and the percentage error is around 6%. The algorithm was also validated performing unmixing of ex vivo data samples from atherosclerotic human tissue containing collagen, elastin and low-density lipoproteins. These experiments were validated against ground-truth maps, which only give a quantitative description, and the estimated accuracy was around 88%.

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RESUMO

This study proposes a novel method to assist the detection of the components that build up the Cyclic Alternating Pattern (CAP). CAP is a sleep phenomenon formed by consecutive sequences of activations (A1, A2, A3) and non-activations during nonREM sleep. The main importance of CAP evaluation is the possibility of defining the sleep process more accurately. Ten recordings from healthy and good sleepers were included in this study. The method is based on inferential statistics to define the initial and ending points of the CAP components based only on an initialization point given by the expert. The results show concordance up to 95% for A1, 85% for A2 and 60% for A3, together with an overestimation of 1.5 s in A1, 1.3 s in A2 and 0 s in A3. The total CAP rate presents a total underestimation of 7 min. Those results suggest that the method is able to accurately detect the initial and ending points of the activations, and may be helpful for the physicians by reducing the time dedicated to the manual inspection task.

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