Evaluación de estrategias basadas en Wavelet-ICA e ICLabel para la corrección de artefactos sobre registros EEG

In quantitative electroencephalography, it is of vital importance to eliminate non-neural components, as these can lead to an erroneous analysis of the acquired signals, limiting their use in diagnosis and other clinical applications. In light of this drawback, preprocessing pipelines based on the joint use of the Wavelet Transform and the Independent Component Analysis technique (wICA) were proposed in the 2000s. Recently, with the advent of data-driven methods, deep learning models were developed for the automatic labeling of independent components, which constitutes an opportunity for the optimization of ICA-based techniques. In this paper, ICLabel, one of these deep learning models, was added to the wICA methodology in order to explore its improvement. To assess the usefulness of this approach, it was compared to different pipelines which feature the use of wICA and ICLabel independently and a lack thereof. The impact of each pipeline was measured by its capacity to highlight known statistical differences between asymptomatic carriers of the PSEN-1 E280A mutation and a healthy control group. Specifically, the between-group effect size and the P-values were calculated to compare the pipelines. The results show that using ICLabel for artifact removal can improve the effect size (ES) and that, by leveraging it with wICA, an artifact smoothing approach that is less prone to the loss of neural information can be built.En la electroencefalografía cuantitativa es de vital importancia la eliminación de componentes no neuronales, ya que estos pueden conducir a un análisis erróneo de las señales adquiridas, limitando su uso al diagnóstico y otras aplicaciones clínicas. Dado este inconveniente, en la década de 2000 se propusieron flujos de preprocesamiento basados ​​en el uso conjunto de la Transformada Wavelet y la técnica de Análisis de Componentes Independientes (wICA). Recientemente, con la llegada de los métodos basados en datos, se desarrollaron modelos de aprendizaje profundo para el etiquetado automático de componentes independientes, lo que generó una oportunidad para la optimización de las técnicas basadas en ICA. En este estudio, se añadió ICLabel, uno de estos modelos de aprendizaje profundo, a la metodología de wICA para explorar su mejora. Para evaluar la utilidad de este enfoque, se comparó con diferentes flujos que muestran el uso de wICA e ICLabel de forma independiente y en su ausencia. El impacto de cada flujo se midió mediante su capacidad para resaltar diferencias estadísticas conocidas entre los portadores asintomáticos de la mutación PSEN-1 E280A y un grupo de control sano. Se calcularon específicamente el tamaño del efecto entre grupos y los valores P para comparar los flujos. Los resultados muestran que el uso de ICLabel para la eliminación de artefactos puede mejorar el tamaño del efecto (ES) y que, al aprovecharlo con wICA, se puede construir un enfoque de suavizado de artefactos menos susceptible a la pérdida de información neuronal

Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings

In quantitative electroencephalography, it is of vital importance to eliminate non-neural components, as these can lead to an erroneous analysis of the acquired signals, limiting their use in diagnosis and other clinical applications. In light of this drawback, preprocessing pipelines based on the joint use of the Wavelet Transform and the Independent Component Analysis technique (wICA) were proposed in the 2000s. Recently, with the advent of data-driven methods, deep learning models were developed for the automatic labeling of independent components, which constitutes an opportunity for the optimization of ICA-based techniques. In this paper, ICLabel, one of these deep learning models, was added to the wICA methodology in order to explore its improvement. To assess the usefulness of this approach, it was compared to different pipelines which feature the use of wICA and ICLabel independently and a lack thereof. The impact of each pipeline was measured by its capacity to highlight known statistical differences between asymptomatic carriers of the PSEN-1 E280A mutation and a healthy control group. Specifically, the between-group effect size and the P-values were calculated to compare the pipelines. The results show that using ICLabel for artifact removal can improve the effect size (ES) and that, by leveraging it with wICA, an artifact smoothing approach that is less prone to the loss of neural information can be built

Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings

In quantitative electroencephalography, it is of vital importance to eliminate non-neural components, as these can lead to an erroneous analysis of the acquired signals, limiting their use in diagnosis and other clinical applications. In light of this drawback, preprocessing pipelines based on the joint use of the Wavelet Transform and the Independent Component Analysis technique (wICA) were proposed in the 2000s. Recently, with the advent of data-driven methods, deep learning models were developed for the automatic labeling of independent components, which constitutes an opportunity for the optimization of ICA-based techniques. In this paper, ICLabel, one of these deep learning models, was added to the wICA methodology in order to explore its improvement. To assess the usefulness of this approach, it was compared to different pipelines which feature the use of wICA and ICLabel independently and a lack thereof. The impact of each pipeline was measured by its capacity to highlight known statistical differences between asymptomatic carriers of the PSEN-1 E280A mutation and a healthy control group. Specifically, the between-group effect size and the P-values were calculated to compare the pipelines. The results show that using ICLabel for artifact removal can improve the effect size (ES) and that, by leveraging it with wICA, an artifact smoothing approach that is less prone to the loss of neural information can be built

Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings

In quantitative electroencephalography, it is of vital importance to eliminate non-neural components, as these can lead to an erroneous analysis of the acquired signals, limiting their use in diagnosis and other clinical applications. In light of this drawback, preprocessing pipelines based on the joint use of the Wavelet Transform and the Independent Component Analysis technique (wICA) were proposed in the 2000s. Recently, with the advent of data-driven methods, deep learning models were developed for the automatic labeling of independent components, which constitutes an opportunity for the optimization of ICA-based techniques. In this paper, ICLabel, one of these deep learning models, was added to the wICA methodology in order to explore its improvement. To assess the usefulness of this approach, it was compared to different pipelines which feature the use of wICA and ICLabel independently and a lack thereof. The impact of each pipeline was measured by its capacity to highlight known statistical differences between asymptomatic carriers of the PSEN-1 E280A mutation and a healthy control group. Specifically, the between-group effect size and the P-values were calculated to compare the pipelines. The results show that using ICLabel for artifact removal can improve the effect size (ES) and that, by leveraging it with wICA, an artifact smoothing approach that is less prone to the loss of neural information can be built

Environmental health at La Chaparrala subbasin, Colombia 2015

RESUMEN: Objetivo Evaluar condiciones de salud ambiental subcuenca la Chaparrala, Andes-Colombia 2015. Método Estudio descriptivo de corte transversal, se indagó sobre condiciones sanitarias y ambientales de las viviendas, entorno inmediato y prejuicios acerca de la salud ambiental, en encuesta a 117 familias asentadas sobre eje principal de la quebrada y se analizó calidad fisicoquímica del agua. Resultados Predominó el hombre como jefe de hogar, cuatro personas en promedio por vivienda, 69 urbanas y 48 rurales, con permanencia mayor a 20 años en las primeras y menor a cinco años en las segundas. La mayoría, propias con escritura, con conexión 75 % al acueducto y 73 % al alcantarillado. En zona urbana predominó techo en losa y piso en baldosa, en la rural, teja de barro y piso en cemento y paredes en adobe en ambas zonas. El agua de la quebrada cumplió parámetros fisicoquímicos establecidos en el reglamento técnico del sector de agua potable y saneamiento básico, y resolución No. 2115 de 2007, excepto Turbiedad y Nitritos y se hallaron ocurrencias y concurrencias sobre salud ambiental. Discusión Las características sociodemográficas y condiciones ambientales y sanitarias de las viviendas y sus alrededores, son similares a las reportadas en encuesta de demografía y salud 2010, y en Encuesta Nacional de Salud (ENDS) 2007. La quebrada puede seguir siendo fuente de abastecimiento para consumo humano con tratamiento convencional y como concurrencia, la salud ambiental con enfoque hacia el cuidado y protección del ambiente que difiere del concepto de Organización Mundial de la Salud (OMS).ABSTRACT: Objective To assess environmental health conditions at La Chaparrala subbasin, in the Andes Mountains of Colombia, during 2015. Method Descriptive cross-sectional study on sanitary and environmental conditions of the dwellings, as well as on immediate environment, and environmental health prejudices. A survey was applied to 117 families settled in the main axis of the creek. The physicochemical quality of the water was analyzed. Results Men were predominant as head of household, with an average of four people per house. 69 of the houses were in the urban area, while 48 were rural; the permanence was greater than 20 years for the first, and less than five years for the second. Most of the houses are owned with deeds, and 75 % of them had a connection to the aqueduct and 73 % to the sewer. The houses in the urban area were predominantly made of tile roof and tile floor, while rural houses were made of mud tile and concrete floor. Both types of construction had adobe walls. The water from the creek complied with the physicochemical parameters established in the technical regulation of the sector for drinking water and basic sanitation, and with resolution No. 2115 of 2007. Non-compliance was observed in turbidity and nitrite levels, and occurrences and concurrences on environmental health were found. Discussion The socio-demographic characteristics and environmental and health conditions of the dwellings and their surroundings are similar to those reported in Encuesta de Demografía y Salud 2010 (Demographic and Health Survey) and Encuesta Nacional de Salud 2007 (2007 National Health Survey). The creek may continue to be a source for human consumption with conventional treatment and environmental health directed to care and protect the environment, differing from the concept of World Health Organization (WHO)

Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis

Objective This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach. Methods We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature. Results For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs. Conclusions Increased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD.publishedVersio

La prueba: teoría y práctica

Especialistas de talla mundial discurren en torno al núcleo básico de la administración de la justicia: la prueba, un tema fundamental que —precisamente por su carácter basilar— tiende a ser pasado por alto o a revisarse de manera superficial. En esta obra colectiva, se estudian —desde una perspectiva tanto conceptual como práctica— problemas fundamentales en torno a la prueba, desde diferentes puntos de vista que se soportan en diversas áreas del conocimiento como la filosofía, la epistemología, la psicología, entre otras. Modestia aparte, no es fortuito que en el prólogo del libro Bujosa Vadell declare que “está llamado a ser obra de cabecera para los juristas que, desde la academia y desde el foro, se preocupan por una conducción adecuada de la actividad probatoria”. Así pues, académicos, juristas, abogados litigantes y jueces tienen a su disposición la décima segunda entrega del Grupo de Investigaciones en Derecho Procesal, de la Universidad de Medellín, reconocido mundialmente por su compromiso y dedicación a la hora de coordinar esta ya tradicional serie de libros de investigación en derecho procesal.https://catalogo.udem.edu.co/la-prueba-teoria-y-practica--derecho-procesal-civil.html#.XbdVx-gzbc