A Review On digital image processing techniques for in-Vivo confocal images of the cornea.

This work reviews the scientific literature regarding digital image processing for in vivo confocal microscopy images of the cornea. We present and discuss a selection of prominent techniques designed for semi- and automatic analysis of four areas of the cornea (epithelium, sub-basal nerve plexus, stroma and endothelium). The main context is image enhancement, detection of structures of interest, and quantification of clinical information. We have found that the preprocessing stage lacks of quantitative studies regarding the quality of the enhanced image, or its effects in subsequent steps of the image processing. Threshold values are widely used in the reviewed methods, although generally, they are selected empirically and manually. The image processing results are evaluated in many cases through comparison with gold standards not widely accepted. It is necessary to standardize values to be quantified in terms of sensitivity and specificity of methods. Most of the reviewed studies do not show an estimation of the computational cost of the image processing. We conclude that reliable, automatic, computer-assisted image analysis of the cornea is still an open issue, constituting an interesting and worthwhile area of research.

Quantitative EEG Tomography of Early Childhood Malnutrition.

The goal of this study is to identify the quantitative electroencephalographic (qEEG) signature of early childhood malnutrition [protein-energy malnutrition (PEM)]. To this end, archival digital EEG recordings of 108 participants in the Barbados Nutrition Study (BNS) were recovered and cleaned of artifacts (46 children who suffered an episode of PEM limited to the first year of life) and 62 healthy controls). The participants of the still ongoing BNS were initially enrolled in 1973, and EEGs for both groups were recorded in 1977-1978 (at 5-11 years). Scalp and source EEG Z-spectra (to correct for age effects) were obtained by comparison with the normative Cuban Human Brain Mapping database. Differences between both groups in the z spectra (for all electrode locations and frequency bins) were assessed by t-tests with thresholds corrected for multiple comparisons by permutation tests. Four clusters of differences were found: (a) increased theta activity (3.91-5.86 Hz) in electrodes T4, O2, Pz and in the sources of the supplementary motor area (SMA); b) decreased alpha1 (8.59-8.98 Hz) in Fronto-central electrodes and sources of widespread bilateral prefrontal are; (c) increased alpha2 (11.33-12.50 Hz) in Temporo-parietal electrodes as well as in sources in Central-parietal areas of the right hemisphere; and (d) increased beta (13.67-18.36 Hz), in T4, T5 and P4 electrodes and decreased in the sources of bilateral occipital-temporal areas. Multivariate Item Response Theory of EEGs scored visually by experts revealed a neurophysiological latent variable which indicated excessive paroxysmal and focal abnormality activity in the PEM group. A robust biomarker construction procedure based on elastic-net regressions and 1000-cross-validations was used to: (i) select stable variables and (ii) calculate the area under ROC curves (AUC). Thus, qEEG differentiate between the two nutrition groups (PEM vs Control) performing as well as visual inspection of the EEG scored by experts (AUC = 0.83). Since PEM is a global public health problem with lifelong neurodevelopmental consequences, our finding of consistent differences between PEM and controls, both in qualitative and quantitative EEG analysis, suggest that this technology may be a source of scalable and affordable biomarkers for assessing the long-term brain impact of early PEM.

Seeking Biomarkers of Early Childhood Malnutrition's Long-term Effects.

Protein-energy malnutrition affects one in nine people worldwide and is most prevalent among children aged less than five years in low-income countries. Early childhood malnutrition can have damaging neurodevelopmental effects, with significant increases in cognitive, neurological and mental health problems over the lifespan, outcomes which can also extend to the next generation. This article describes a research collaboration involving scientists from five centers in Barbados, China, Cuba and the USA. It builds on longer-term joint work between the Barbados Nutrition Study (which, over a 45-year span, has extensively documented nutritional, health, behavioral, social and economic outcomes of individuals who experienced protein-energy malnutrition in the first year of life and healthy controls from the same classrooms and neighborhoods) and the Cuban Neuroscience Center (which has developed low-cost brain imaging methods that can be readily used in low income settings to identify biomarkers for early detection and treatment of adverse consequences of childhood malnutrition). This collaboration, which involved Barbadian, Cuban and US scientists began in the 1970s, when quantitative EEG techniques were applied to EEG data collected in 1977-78, at which time study participants were aged 5-11 years. These EEG records were never fully analyzed but were stored in New York and made available to this project in 2016. These data have now been processed and analyzed, comparing EEG findings in previously malnourished and control children, and have led to the identification of early biomarkers of long-term effects of early childhood protein-energy malnutrition. The next stage of the project will involve extending earlier work by collecting EEG recordings in the same individuals at ages 45-51 years, 40 years later, and comparing findings to earlier data and to these individuals' behavioral and cognitive outcomes. Quantitative EEG biomarkers of the effects of protein-energy malnutrition may help identify children at greatest risk for early malnutrition's adverse neurodevelopmental effects and inform development of targeted interventions to mitigate the long-term adverse effects of protein-energy malnutrition in developing countries. KEYWORDS Protein-energy malnutrition, electroencephalography, EEG, biomarkers, neurosciences, Barbados, Cuba, USA.

Cancelador adaptativo de interferencias con mínima distorsión / Interference adaptive canceller with minimum

La interferencia de la línea de alimentación y sus armónicos es una de las fuentes más importantes de contaminación de las señales bioeléctricas adquiridas con electrodos superficiales. Esta interferencia debe tratar de evitarse desde el proceso de adquisición de las señales porque pudiera enmascarar totalmente eventos de interés para el diagnóstico. Lamentablemente, las medidas tomadas durante la adquisición no son siempre exitosas y pueden prevalecer niveles considerables de interferencia que deben ser eliminados procesando la señal digital.Dentro de los procedimientos para atenuar la interferencia de la línea de alimentación y sus armónicos figuran los filtros digitales pasabanda de banda estrecha (conocidos como notch) y algoritmos de cancelación adaptativos, con criterios de corrección LMS y RLS. Estosconocidos algoritmos, no obstante, introducen distorsiones notables en la señal filtrada, sobre todo en regiones que siguen a los cambios bruscos en la señal (pendientes pronunciadas, como las del Q R S en la señal electrocardiográfica). Esa distorsión está relacionada con un efecto oscilatorio (ringing) que puede ser prohibitivo en ciertos análisis, como el de los potenciales tardíos ventriculares en la señal electrocardiográfica de alta resolución. En este trabajo se propone un novedoso cancelador adaptativo RLS que detiene la adaptación de los coeficientes durante los segmentos de alta pendiente. El desempeño de este algoritmo es comparado con el de las otras técnicas de cancelación de interferencias convencionales, mostrando evidente superioridad. Para la evaluación de losalgoritmos se utilizaron señales semi-simuladas y señales electrocardiográficas de alta resolución reales, pero el nuevo cancelador puede tener otras muchas aplicaciones(AU)