Continuous wavelet transform in the study of the time-scale properties of intracranial pressure in hydrocephalus

[EN]Normal pressure hydrocephalus (NPH) encompasses a heterogeneous group of disorders generally characterized by clinical symptoms, ventriculomegaly and anomalous cerebrospinal fluid (CSF) dynamics. Lumbar infusion tests (ITs) are frequently performed in the preoperatory evaluation of patients who show NPH features. The analysis of intracranial pressure (ICP) signals recorded during ITs could be useful to better understand the pathophysiology underlying NPH and to assist treatment decisions. In this study, 131 ICP signals recorded during ITs were analysed using two continuous wavelet transform (CWT)- derived parameters: Jensen divergence (JD) and spectral flux (SF). These parameters were studied in two frequency bands, associated with different components of the signal: B1(0.150.3 Hz), related to respiratory blood pressure oscillations; and B2 (0.672.5 Hz), related to ICP pulse waves. Statistically significant differences (p1.7010-3, Bonferronicorrected Wilcoxon signed-rank tests) in pairwise comparisons between phases of ITs were found using the mean and standard deviation of JD and SF. These differences were mainly found in B2, where a lower irregularity and variability, together with less prominent time-frequency fluctuations, were found in the hypertension phase of ITs. Our results suggest that wavelet analysis could be useful for understanding CSF dynamics in NPH. This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'

Quantitative evaluation of artifact removal in real magnetoencephalogram signals with blind source separation

The magnetoencephalogram (MEG) is contaminated with undesired signals, which are called artifacts. Some of the most important ones are the cardiac and the ocular artifacts (CA and OA, respectively), and the power line noise (PLN). Blind source separation (BSS) has been used to reduce the influence of the artifacts in the data. There is a plethora of BSS-based artifact removal approaches, but few comparative analyses. In this study, MEG background activity from 26 subjects was processed with five widespread BSS (AMUSE, SOBI, JADE, extended Infomax, and FastICA) and one constrained BSS (cBSS) techniques. Then, the ability of several combinations of BSS algorithm, epoch length, and artifact detection metric to automatically reduce the CA, OA, and PLN were quantified with objective criteria. The results pinpointed to cBSS as a very suitable approach to remove the CA. Additionally, a combination of AMUSE or SOBI and artifact detection metrics based on entropy or power criteria decreased the OA. Finally, the PLN was reduced by means of a spectral metric. These findings confirm the utility of BSS to help in the artifact removal for MEG background activity

Brain-computer interface channel selection optimization using meta-heuristics and evolutionary algorithms

Producción CientíficaMany brain–computer interface (BCI) studies overlook the channel optimization due to its inherent complexity. However, a careful channel selection increases the performance and users’ comfort while reducing the cost of the system. Evolutionary meta-heuristics, which have demonstrated their usefulness in solving complex problems, have not been fully exploited yet in this context. The purpose of the study is two-fold: (1) to propose a novel algorithm to find an optimal channel set for each user and compare it with other existing meta-heuristics; and (2) to establish guidelines for adapting these optimization strategies to this framework. A total of 3 single-objective (GA, BDE, BPSO) and 4 multi-objective (NSGA-II, BMOPSO, SPEA2, PEAIL) existing algorithms have been adapted and tested with 3 public databases: ‘BCI competition III–dataset II’, ‘Center Speller’ and ‘RSVP Speller’. Dual-Front Sorting Algorithm (DFGA), a novel multi-objective discrete method especially designed to the BCI framework, is proposed as well. Results showed that all meta-heuristics outperformed the full set and the common 8-channel set for P300-based BCIs. DFGA showed a significant improvement of accuracy of 3.9% over the latter using also 8 channels; and obtained similar accuracies using a mean of 4.66 channels. A topographic analysis also reinforced the need to customize a channel set for each user. Thus, the proposed method computes an optimal set of solutions with different number of channels, allowing the user to select the most appropriate distribution for the next BCI sessions.Ministerio de Ciencia, Innovación y Universidades (project RTC2019-007350-1)Comisión Europea (project 0702_MIGRAINEE_2_E

Simple low-power demodulator for the measurement of basal and physiological changes of electrical bioimpedance

Wearable sensors constitute a growing trend both as a research tool as well as an end-consumer products. In the physiological signal monitoring fields, the changing signals have a low bandwidth hence most sensing techniques are not powerhungry. Electrical bioimpedance is a non-obtrusive sensing technique and is an interesting choice as it can recover both breathing and pulse rate signals. However, it works at comparatively higher frequencies and usually need demodulation circuitry, meaning higher power consumption. In this work, we show an alternative technique to demodulate the impedance response that need very few components and whose power consumption can be tailored as needed. We show that, by using a 24-bit ADC, the respiration and pulse rate signals as well as the basal impedance are recovered from the demodulated signal and further digital band-pass filters that are easily implemented with currently available low-power microcontroller technology. Further, it constitutes a compact solution, as the sensor needs not to change location to measure these signals.Peer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (published version

Continuous wavelet transform in the study of the time-scale properties of intracranial pressure in hydrocephalus

Producción CientíficaNormal pressure hydrocephalus (NPH) encompasses a heterogeneous group of disorders generally characterised by clinical symptoms, ventriculomegaly and anomalous cerebrospinal fluid (CSF) dynamics. Lumbar infusion tests (ITs) are frequently performed in the preoperatory evaluation of patients who show NPH features. The analysis of intracranial pressure (ICP) signals recorded during ITs could be useful to better understand the pathophysiology underlying NPH and to assist treatment decisions. In this study, 131 ICP signals recorded during ITs were analysed using two continuous wavelet transform (CWT)-derived parameters: Jensen Divergence (JD) and Spectral Flux (SF). These parameters were studied in two frequency bands, associated with different components of the signal: "(0.15 - 0.3 Hz), related to respiratory blood pressure oscillations; and # (0.67 - 2.5 Hz), related to ICP pulse waves. Statistically significant differences ( < 1.70 ∙ 10+,, Bonferroni-corrected Wilcoxon signed rank tests) in pairwise comparisons between phases of ITs were found using the mean and standard deviation of JD and SF. These differences were mainly found in #, where a lower irregularity and variability, together with less prominent time-frequency fluctuations, were found in the hypertension phase of ITs. Our results suggest that wavelet analysis could be useful for understanding CSF dynamics in NPH.This research was supported by ‘Ministerio de Economía y Competitividad’ and 'European Regional Development Fund' (FEDER) under project TEC2014-53196-R, by ‘European Commission’ and 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'), and by ‘Consejería de Educación de la Junta de Castilla y León’ and FEDER under project VA037U16

XIV Foro Internacional sobre la Evaluación de la Calidad de la Investigación y de la Educación Superior (FECIES)

Introducción. El Proceso de Bolonia ha impulsado un nuevo modelo educativo que pretende centrar las metodologías docentes en el aprendizaje de los estudiantes, priorizando el manejo de herramientas por encima de la mera acumulación de conocimientos. Objetivo. El objetivo de este trabajo es evaluar la eficacia del aprendizaje colaborativo en la asignatura “Sistemas de Comunicación”, que se imparte en la E.T.S. Ingenieros de Telecomunicación de la Universidad de Valladolid. Método. Se emplearon diferentes herramientas basadas en el aprendizaje colaborativo dentro de la “Práctica sobre telefonía móvil de cuarta generación”. Dicha práctica está compuesta por diferentes actividades: (i) Trabajo escrito individual; (ii) Actividad de revisión por pares; (iii) Debate; (iv) Actividad puzle; (v) Trabajo escrito grupal; y (vi) Exposición y defensa del trabajo grupal. Resultados. Las encuestas realizadas a los alumnos mostraron que están muy satisfechos con esta práctica, puesto que les ha permitido desarrollar diferentes competencias transversales. Además, los alumnos obtuvieron una mayor calificación en el examen final de la asignatura en la pregunta sobre telefonía móvil de cuarta generación (Calificación sobre 1 punto = 0.59 0.33 puntos, media desviación estándar) que en la pregunta análoga sobre telefonía móvil de tercera generación, tema impartido utilizando una metodología docente tradicional (Calificación sobre 1 punto = 0.51 0.34 puntos). Conclusiones. El aprendizaje colaborativo no sólo permite a los alumnos adquirir competencias transversales, sino que además les permite interiorizar más conocimientos teóricos que la docencia tradicional

Classification of alzheimer’s electroencephalograms using artificial neural networks and logistic regression

The Artificial Neural Networks have been used over the years to solve complex problems and their development has strongly grown in recent years. In particular, this work, focused on the development and a comparison between Artificial Neural Networks (ANN) and a traditional statistical technic known as Logistic Regression (LR) in Electroencephalogram (EEG) classification. The Wavelet Transform was seen as the main technique of signal processing, in order to analyze the EEG signals of this study. Some features were extracted by the EEG signals like relative power (RP) in conventional frequency bands and two spectral ratios. The best feature combination was selected by Principal Components Analysis method to increase the accuracy of the ANN and LR to discriminate their entries between Alzheimer Disease and Controls

Measuring Alterations of Spontaneous EEG Neural Coupling in Alzheimer's Disease and Mild Cognitive Impairment by Means of Cross-Entropy Metrics

Alzheimer's Disease (AD) represents the most prevalent form of dementia and is considered a major health problem due to its high prevalence and its economic costs. An accurate characterization of the underlying neural dynamics in AD is crucial in order to adopt effective treatments. In this regard, mild cognitive impairment (MCI) is an important clinical entity, since it is a risk-state for developing dementia. In the present study, coupling patterns of 111 resting-state electroencephalography (EEG) recordings were analyzed. Specifically, we computed Cross-Approximate Entropy (Cross-ApEn) and Cross-Sample Entropy (Cross-SampEn) of 37 patients with dementia due to AD, 37 subjects with MCI, and 37 healthy control (HC) subjects. Our results showed that Cross-SampEn outperformed Cross-ApEn, revealing higher number of significant connections among the three groups (Kruskal-Wallis test, FDR-corrected p-values &lt; 0.05). AD patients exhibited statistically significant lower similarity values at θ and β1 frequency bands compared to HC. MCI is also characterized by a global decrease of similarity in all bands, being only significant at β1. These differences shows that β band might play a significant role in the identification of early stages of AD. Our results suggest that Cross-SampEn could increase the insight into brain dynamics at different AD stages. Consequently, it may contribute to develop early AD biomarkers, potentially useful as diagnostic information