An invasive and a noninvasive approach for the automatic differentiation of obstructive and central hypopneas

The automatic 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. This study presents a new classifier that automatically differentiates obstructive and central hypopneas with the Pes signal and a new approach for an automatic noninvasive classifierwith nasal airflow. An overall of 28 patients underwent night polysomnography with Pes recording, and a total of 769 hypopneas were manually scored by human experts to create a gold-standard annotation set. Features were automatically extracted fromthe Pes signal to train and test the classifiers (discriminant analysis, support vector machines, and adaboost). After a significantly (p < 0.01) higher incidence of inspiratory flow limitation episodes in obstructive hypopneas was objectively, invasively assessed compared to central hypopneas, the feasibility of an automatic noninvasive classifier with features extracted from the airflow signal was demonstrated. The automatic invasive classifier achieved a mean sensitivity, specificity, and accuracy of 0.90 after a 100-fold cross validation. The automatic noninvasive feasibility study obtained similar hypopnea differentiation results as a manual noninvasive classification algorithm. Hence, both systems seem promising for the automatic differentiation of obstructive and central hypopneas.Peer ReviewedPostprint (published version

Real time geotechnical field data acquistion using a distributed approach

A distributed geotechnical remote analysis of data system (Distributed G-RAD) can benefit both owners and contractors in providing better quality control and assurance on geotechnical projects. The Distributed G-RAD approach involves efficient data acquisition using PDAs with GPS capability, radio frequency identification (RFID) tags for labeling soil samples, laser scanning for measuring lift thickness and volumes of stockpiles and borrow pits. Spatial data storage is provided using a geographic information system (GIS). Portions of this system are already developed while other parts are still being considered. This paper also describes how RFID and laser scanning technologies can be used in the larger Distributed G-RAD system

Axonal Transmission in the Retina Introduces a Small Dispersion of Relative Timing in the Ganglion Cell Population Response

Background: Visual stimuli elicit action potentials in tens of different retinal ganglion cells. Each ganglion cell type responds with a different latency to a given stimulus, thus transforming the high-dimensional input into a temporal neural code. The timing of the first spikes between different retinal projection neurons cells may further change along axonal transmission. The purpose of this study is to investigate if intraretinal conduction velocity leads to a synchronization or dispersion of the population signal leaving the eye. Methodology/Principal Findings: We 'imaged' the initiation and transmission of light-evoked action potentials along individual axons in the rabbit retina at micron-scale resolution using a high-density multi-transistor array. We measured unimodal conduction velocity distributions (1.3 +/- 0.3 m/sec, mean +/- SD) for axonal populations at all retinal eccentricities with the exception of the central part that contains myelinated axons. The velocity variance within each piece of retina is caused by ganglion cell types that show narrower and slightly different average velocity tuning. Ganglion cells of the same type respond with similar latency to spatially homogenous stimuli and conduct with similar velocity. For ganglion cells of different type intraretinal conduction velocity and response latency to flashed stimuli are negatively correlated, indicating that differences in first spike timing increase (up to 10 msec). Similarly, the analysis of pair-wise correlated activity in response to white-noise stimuli reveals that conduction velocity and response latency are negatively correlated. Conclusion/Significance: Intraretinal conduction does not change the relative spike timing between ganglion cells of the same type but increases spike timing differences among ganglion cells of different type. The fastest retinal ganglion cells therefore act as indicators of new stimuli for postsynaptic neurons. The intraretinal dispersion of the population activity will not be compensated by variability in extraretinal conduction times, estimated from data in the literature

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Diferenciación automática de hipopneas mediante la señal de presión esofágica durante el sueño

La diferenciación entre hipopneas obstructivas y centrales es uno de los trabajos más recurrentes en el diagnóstico de los trastornos respiratorios del sueño. La medición de presión esofágica (Pes) es el método gold-standard para medir el esfuerzo respiratorio e identificar estos eventos. Pero al ser una técnica invasiva su uso en la rutina clínica está muy limitado. Por ello se han ido proponiendo sistemas no-invasivos para la diferenciación de eventos respiratorios. Sin embargo su adopción ha sido lenta debido a limitaciones en su validación, ya que la creación de un set de validación mediante la clasificación manual por un experto humano es una tarea poco eficiente. En el presente trabajo se propone un nuevo sistema para la diferenciación automática y objetiva entre hipopneas centrales y obstructivas mediante la señal gold-standard de Pes. Un experto humano clasificó manualmente en un primer paso 356 hipopneas de 16 pacientes para crear un set de validación gold-standard. Entonces se extrajeron automáticamente varios parámetros de cada hipopnea para entrenar y testear clasificadores (Análisis Discriminante, Support Vector Machines y adaboost) para diferenciar las hipopneas con la señal de Pes gold-standard. La diferenciación automática de nuestros clasificadores obtuvo unos resultados prometedores, obteniendo una sensibilidad de 0.88, una especificidad de 0.93 y una exactitud de 0.90. Este sistema parece ser por tanto prometedor para una diferenciación automática entre hipopneas obstructivas y centrales.Peer ReviewedPostprint (published version

Comparison of upper airway respiratory resistance measurements with the esophageal pressure/airflow relationship during sleep

Measurement of upper airway resistance is of interest in sleep disordered breathing to estimate upper airway patency. Resistance is calculated with the airflow and respiratory effort signals. However, there is no consensus on a standard for upper airway resistance measurement. This study proposes a new benchmarking method to objectively compare different upper airway resistance measurement methods by objectively differentiating between breaths with inspiratory flow limitation (high resistance) and non-limited breaths (low resistance). Resistance was measured at peak-Pes, at peak-flow, at the linear portion of a polynomial equation, as an area comparative and as average resistance for an inspiration. A total of 20 patients with systematic, gold-standard esophageal pressure and nasal airflow acquisition were analyzed and 109,955 breaths were automatically extracted and evaluated. Relative resistance values in relationship to a reference resistance value obtained during wakefulness were also analyzed. The peak-Pes measurement method obtained the highest separation index with significant (p < 0.001) differences to the other methods, followed by the area comparative and the peak-flow methods. As expected, average resistances were significantly (p < 0.001) lower for the non-IFL than for the IFL group. Hence, we recommend employing the peak-Pes for accurate upper airway resistance estimation.Peer ReviewedPostprint (published version

Feasibility of noninvasive single-channel automated differentiation of obstructive and central hypopneas with nasal airflow

Background: The identification of obstructive and central hypopneas is considered challenging in clinical practice. Presently, obstructive and central hypopneas are usually not differentiated or scores lack reliability due to the technical limitations of standard polysomnography. Esophageal pressure measurement is the gold-standard for identifying these events but its invasiveness deters its usage in daily practice. Objectives: To determine the feasibility and efficacy of an automatic noninvasive analysis method for the differentiation of obstructive and central hypopneas based solely on a single-channel nasal airflow signal. The obtained results are compared with gold-standard esophageal pressure scores. Methods: A total of 41 patients underwent full night polysomnography with systematic esophageal pressure recording. Two experts in sleep medicine independently differentiated hypopneas with the gold-standard esophageal pressure signal. Features were automatically extracted from the nasal airflow signal of each annotated hypopnea to train and test the automatic analysis method. Interscorer agreement between automatic and visual scorers was measured with Cohen’s kappa statistic (κ). Results: A total of 1,237 hypopneas were visually differentiated. The automatic analysis achieved an interscorer agreement of κ = 0.37 and an accuracy of 69% for scorer A, κ = 0.40 and 70% for scorer B and κ = 0.41 and 71% for the agreed scores of scorers A and B. Conclusions: The promising results obtained in this pilot study demonstrate the feasibility of noninvasive single-channel hypopnea differentiation. Further development of this method may help improving initial diagnosis with home screening devices and offering a means of therapy selection and/or control.Peer ReviewedPostprint (published version

Diferenciación automática de hipopneas mediante la señal de presión esofágica durante el sueño

La diferenciación entre hipopneas obstructivas y centrales es uno de los trabajos más recurrentes en el diagnóstico de los trastornos respiratorios del sueño. La medición de presión esofágica (Pes) es el método gold-standard para medir el esfuerzo respiratorio e identificar estos eventos. Pero al ser una técnica invasiva su uso en la rutina clínica está muy limitado. Por ello se han ido proponiendo sistemas no-invasivos para la diferenciación de eventos respiratorios. Sin embargo su adopción ha sido lenta debido a limitaciones en su validación, ya que la creación de un set de validación mediante la clasificación manual por un experto humano es una tarea poco eficiente. En el presente trabajo se propone un nuevo sistema para la diferenciación automática y objetiva entre hipopneas centrales y obstructivas mediante la señal gold-standard de Pes. Un experto humano clasificó manualmente en un primer paso 356 hipopneas de 16 pacientes para crear un set de validación gold-standard. Entonces se extrajeron automáticamente varios parámetros de cada hipopnea para entrenar y testear clasificadores (Análisis Discriminante, Support Vector Machines y adaboost) para diferenciar las hipopneas con la señal de Pes gold-standard. La diferenciación automática de nuestros clasificadores obtuvo unos resultados prometedores, obteniendo una sensibilidad de 0.88, una especificidad de 0.93 y una exactitud de 0.90. Este sistema parece ser por tanto prometedor para una diferenciación automática entre hipopneas obstructivas y centrales.Peer Reviewe