Electroencephalogram background activity characterization with Detrended Moving Average in Alzheimer's disease patients

The aim of this study was to analyse the electroencephalogram (EEG) background activity in Alzheimer's disease (AD) with the Detrended Moving Average (DMA) method, a new approach to quantify correlation properties in non-stationary signals with underlying trends. EEGs were recorded from the 19 scalp loci of the international 10-20 system in 11 AD patients and 11 age-matched controls. Our results showed two scaling regions in all subjects' channels, with a clear bend when their corresponding slopes (alpha(1) and alpha(2)) were distinctly different. With the exception of electrode T4, the alpha(1) values were lower in control subjects than in AD patients, with significant differences at TS, P3, P4 and O1 (p < 0.01, Student's t-test). On the other hand, alpha(2) values were higher in control subjects than in AD patients, with significant differences only at F4. Furthermore, we evaluated the ability of alpha(2) to discriminate AD patients from control subjects at these electrodes using ROC plots. We obtained a maximum accuracy of 81.82% at O1 with alpha(1) and at F4 with alpha(2). These findings suggest that the scaling behaviour of the EEG is sensitive to AD and that the DMA method could help to increase our insight into brain dysfunction in AD

EEG characterization of the Alzheimer's disease continuum by means of multiscale entropies

Alzheimer's disease (AD) is a neurodegenerative disorder with high prevalence, known for its highly disabling symptoms. The aim of this study was to characterize the alterations in the irregularity and the complexity of the brain activity along the AD continuum. Both irregularity and complexity can be studied applying entropy-based measures throughout multiple temporal scales. In this regard, multiscale sample entropy (MSE) and refined multiscale spectral entropy (rMSSE) were calculated from electroencephalographic (EEG) data. Five minutes of resting-state EEG activity were recorded from 51 healthy controls, 51 mild cognitive impaired (MCI) subjects, 51 mild AD patients (ADMIL), 50 moderate AD patients (ADMOD), and 50 severe AD patients (ADSEV). Our results show statistically significant differences (p-values < 0.05, FDR-corrected Kruskal-Wallis test) between the five groups at each temporal scale. Additionally, average slope values and areas under MSE and rMSSE curves revealed significant changes in complexity mainly for controls vs. MCI, MCI vs. ADMIL and ADMOD vs. ADSEV comparisons (p-values < 0.05, FDR-corrected Mann-Whitney U-test). These findings indicate that MSE and rMSSE reflect the neuronal disturbances associated with the development of dementia, and may contribute to the development of new tools to track the AD progression.This research was supported by European Commission and European Regional Development Fund (FEDER) under project “Análisis y correlación entre el genoma completo y la actividad cerebral para la ayuda en el diagnóstico de la enfermedad de Alzheimer” (Cooperation Programme Interreg V-A Spain-Portugal, POCTEP 2014-2020); by “Ministerio de Ciencia, Innovación y Universidades” and FEDER under projects PGC2018-098214-A-I00 and DPI2017-84280-R; and by “Fundação para a Ciência e a Tecnologia/Ministério da Ciência, Tecnologia e Inovação” and FEDER under projects POCI-01-0145-FEDER-007274 and UID/MAT/00144/2013

Optimized assessment of atrial fibrillation organization through suitable parameters of Sample Entropy

Sample Entropy (SampEn) is a nonlinear regularity index that requires the a priori selection of three parameters: the length of the sequences to be compared, m, the patterns similarity tolerance, r, and the number of samples under analysis, N. Appropriate values for m, r and N have been recommended in some cases, such as heart rate, hormonal data, etc., but no guidelines exist for the selection of that values. Hence, an optimal parameters study should be required for the application of SampEn to not previously analyzed biomedical signals. In this work, a thorough analysis on the optimal SampEn parameter values within two different scenarios of AF organization estimation, such as the prediction of paroxysmal AF termination and the electrical cardioversion outcome in persistent AF, is presented. Results indicated that, (i) the proportion between N and the sampling rate (ƒ(s)) should be higher than one second and ƒ(s) ≥ 256 Hz, (ii) overlapping between adjacent N-length windows does not improve organization estimation and (iii) values of m and r maximizing classification should be considered within a range wider than the proposed in the literature for heart rate analysis, i. e. m = 1 and m = 2 and r between 0.1 and 0.25 times the standard deviation of the data

Regional coherence evaluation in mild cognitive impairment and Alzheimer's disease based on adaptively extracted magnetoencephalogram rhythms

This study assesses the connectivity alterations caused by Alzheimer's disease (AD) and mild cognitive impairment (MCI) in magnetoencephalogram (MEG) background activity. Moreover, a novel methodology to adaptively extract brain rhythms from the MEG is introduced. This methodology relies on the ability of empirical mode decomposition to isolate local signal oscillations and constrained blind source separation to extract the activity that jointly represents a subset of channels. Inter-regional MEG connectivity was analysed for 36 AD, 18 MCI and 26 control subjects in δ, θ, α and β bands over left and right central, anterior, lateral and posterior regions with magnitude squared coherence—c(f). For the sake of comparison, c(f) was calculated from the original MEG channels and from the adaptively extracted rhythms. The results indicated that AD and MCI cause slight alterations in the MEG connectivity. Computed from the extracted rhythms, c(f) distinguished AD and MCI subjects from controls with 69.4% and 77.3% accuracies, respectively, in a full leave-one-out cross-validation evaluation. These values were higher than those obtained without the proposed extraction methodology

CO33 188. Abordaje mínimamente invasivo frente a abordaje estándar en cirugía de recambio valvular aórtico

ObjetivoComparar resultados obtenidos en pacientes sometidos a recambio valvular aórtico aislado por abordaje mínimamente invasivo frente a esternotomía estándar.MétodosEntre enero de 2006 – diciembre de 2011, 524 pacientes fueron sometidos de forma programada a recambio valvular aórtico aislado, de los cuales 454 fueron realizados mediante abordaje estándar (grupo E) y 70 mediante miniesternotomía en «J» (grupo M). Consideradas variables preoperatorias, tiempos de circulación extracorpórea (CEC) y clampaje aórtico, resultados postoperatorios (morbimortalidad, estancias en unidad de cuidados intensivos [UCI] y postoperatoria total) y coste económico global (estancias en UCI, sala, intervención quirúrgica, consumo de fungible, implante y otros recursos).ResultadosLas características preoperatorias de la población fueron similares, sin diferencias significativas en cuanto a edad (68±9 vs 69±8 años) y EuroSCORE I aditivo (6,04±3,00 vs 5,38±2,23) entre grupo E y M, respectivamente. Sin embargo sí hubo diferencias significativas en cuanto a mortalidad (4,2 vs 0%; p < 0,005), tiempo de CEC (95±38 vs 84±21 min; p < 0,001), tiempo de clampaje aórtico (72±28 vs 65±15 min; p < 0,001), días de estancia en UCI (4,31±5,27 vs 3,14±1,2; p < 0,05) y días totales de estancia hospitalaria (9,68±7,6 vs 7,87±4,0; p < 0,05) entre grupo E y M, respectivamente. El grupo M presentó un consumo de 1.771,21 €/paciente inferior al grupo E.ConclusiónLa cirugía mínimamente invasiva para el recambio valvular aórtico puede ser beneficiosa tanto en términos de morbimortalidad como en términos económicos. Dado que el estudio presentado es retrospectivo, creemos que futuros análisis prospectivos aleatorizados serían convenientes para profundizar los hallazgos obtenidos

Study of Sample Entropy Ideal Computational Parameters in the Estimation of Atrial Fibrillation Organization from the ECG

Introduction The application of nonlinear regularity metrics to physiological signals is a valuable tool because &quot;hidden information&quot; related to underlying mechanisms can be obtained Given a time series with N data points, the a priori selection of two unknown parameters, m and r, is required to compute SampEn Although these parameters are critical in determining the outcome of SampEn, no guidelines exist for optimizing their values. Typically recommended m and r values are m = 1 and m = 2 and r between 0.1 and 0.25 times the standard deviation (SD) of the data Materials Two different scenarios, such as the prediction of paroxysmal AF termination the electrical cardioversion (ECV) outcome in persistent AF, in which organization plays an important role, were analyzed. Regarding paroxysmal AF, 50 Holter recordings of 30 seconds in length and two leads (II and V1) available in Physionet [8] were analyzed. The database included nonterminating AF episodes (group N), which were observed to continue in AF for, at least, one hour following the end of the excerpt, and AF episodes terminating immediately after the end of the extracted segment (group T). These signals were digitized at a sampling rate of 128 Hz and 16-bit resolution. With regard to persistent AF, 63 patients with persistent AF lasting more than 30 days, undergoing ECV were followed during four weeks. A standard 12-lead ECG was acquired for each patient during the whole procedure and a segment of 30 seconds in length was extracted from each recording for the analysis. All the signals were digi

Lempel-Ziv Complexity Analysis for the Evaluation of Atrial Fibrillation Organization

The Lempel-Ziv (LZ) complexity is a non-linear time series analysis metric that reflects the arising rate of new patterns along with the sequence. Thus, it captures its temporal sequence and, quite conveniently, it can be computed with short data segments. In the present work, a detailed analysis on LZ complexity is presented within the context of atrial fibrillation (AF) organization estimation. As the analysed time series depend on the original sampling rate (fs), we evaluated the relationship between LZ complexity and fs. Furthermore, different implementations of LZ complexity were tested. Our results show the usefulness of LZ complexity to estimate AF organization and suggest that the signals from a terminating paroxysmal AF group are more organized (i.e. less complex) than those from the non-terminating paroxysmal AF group. However, the diagnostic accuracy was not as high as that obtained with sample entropy (SampEn), another non-linear metric, with the same database in a previous study (92% vs. 96%). Nevertheless, the LZ complexity analysis of AF organization with sampling frequencies higher than 2048 Hz, or even its combination with SampEn or other non-linear metrics, might improve the prediction of spontaneous AF termination

Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy

The aim of this study was to analyse the electroencephalogram (EEG) background activity of Alzheimer’s disease (AD) patients using the Multiscale Entropy (MSE). The MSE is a recently developed method that quantifies the regularity of a signal on different time scales. These time scales are inspected by means of several coarse-grained sequences formed from the analysed signals. We recorded the EEGs from 19 scalp electrodes in 11 AD patients and 11 age-matched controls and estimated the MSE profile for each epoch of the EEG recordings. The shape of the MSE profiles reveals the EEG complexity, and it suggests that the EEG contains information in deeper scales than the smallest one. Moreover, the results showed that the EEG background activity is less complex in AD patients than control subjects. We found significant difference

Assessing the contribution of understory sun-induced chlorophyll fluorescence through 3-D radiative transfer modelling and field data

A major international effort has been made to monitor sun-induced chlorophyll fluorescence (SIF) from space as a proxy for the photosynthetic activity of terrestrial vegetation. However, the effect of spatial heterogeneity on the SIF retrievals from canopy radiance derived from images with medium and low spatial resolution remains uncharacterised. In images from forest and agricultural landscapes, the background comprises a mixture of soil and understory and can generate confounding effects that limit the interpretation of the SIF at the canopy level. This paper aims to improve the understanding of SIF from coarse spatial resolutions in heterogeneous canopies by considering the separated contribution of tree crowns, understory and background components, using a modified version of the FluorFLIGHT radiative transfer model (RTM). The new model is compared with others through the RAMI model intercomparison framework and is validated with airborne data. The airborne campaign includes high-resolution data collected over a tree-grass ecosystem with the HyPlant imaging spectrometer within the FLuorescence EXplorer (FLEX) preparatory missions. Field data measurements were collected from plots with a varying fraction of tree and understory vegetation cover. The relationship between airborne SIF calculated from pure tree crowns and aggregated pixels shows the effect of the understory at different resolutions. For a pixel size smaller than the mean crown size, the impact of the background was low (R2 > 0.99; NRMSE 0.2). This study demonstrates that using a 3D RTM model improves the calculation of SIF significantly (R2 = 0.83, RMSE = 0.03 mW m−2 sr−1 nm−1) when the specific contribution of the soil and understory layers are accounted for, in comparison with the SIF calculated from mixed pixels that considers only one layer as background (R2 = 0.4, RMSE = 0.28 mW m−2 sr−1 nm−1). These results demonstrate the need to account for the contribution of SIF emitted by the understory in the quantification of SIF within tree crowns and within the canopy from aggregated pixels in heterogeneous forest canopies