A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals

Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which result from the synchronous and excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an important problem in biomedical signal processing. In this work we propose a new algorithm for seizure onset detection and spread estimation in epilepsy patients. The algorithm is based on a multilevel 1-D wavelet decomposition that captures the physiological brain frequency signals coupled with a generalized gaussian model. Preliminary experiments with signals from 30 epilepsy crisis and 11 subjects, suggest that the proposed methodology is a powerful tool for detecting the onset of epilepsy seizures with his spread across the brain.Fil: Antonio Quintero, Rincón. Instituto Tecnológico de Buenos Aires; ArgentinaFil: Pereyra, Marcelo Fabián. University of Bristol; Reino UnidoFil: D'Giano, Carlos. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Batatia, Hadj. Instituto Polytechnique de Toulouse; Francia. University of Toulouse; FranciaFil: Risk, Marcelo. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Review of the methods of determination of directed connectivity from multichannel data

The methods applied for estimation of functional connectivity from multichannel data are described with special emphasis on the estimators of directedness such as directed transfer function (DTF) and partial directed coherence. These estimators based on multivariate autoregressive model are free of pitfalls connected with application of bivariate measures. The examples of applications illustrating the performance of the methods are given. Time-varying estimators of directedness: short-time DTF and adaptive methods are presented

Time‐Series Prediction Approaches to Forecasting Deformation in Sentinel‐1 InSAR Data

Time series of displacement are now routinely available from satellite InSAR and are used for flagging anomalous ground motion, but not yet forecasting. We test conventional time series forecasting methods such as SARIMA and supervised machine learning approaches such as LSTM compared to simple function extrapolation. We focus initially on forecasting seasonal signals and begin by characterising the time‐series using sinusoid fitting, seasonal decomposition and autocorrelation functions. We find that the three measures are broadly comparable but identify different types of seasonal characteristic. We use this to select a set of 310 points with highly seasonal characteristics and test the three chosen forecasting methods over prediction windows of 1‐9 months. The lowest overall median RMSE values are obtained for SARIMA when considering short term predictions ( 6 months). Machine learning methods (LSTM) perform less well. We then test the prediction methods on 2000 randomly selected points with a range of seasonalities and find that simple extrapolation of a constant function performed better overall than any of the more sophisticated time series prediction methods. Comparisons between seasonality and RMSE show a small improvement in performance with increasing seasonality. This proof‐of‐concept study demonstrates the potential of time‐series prediction for InSAR data but also highlights the limitations of applying these techniques to non‐periodic signals or individual measurement points. We anticipate future developments, especially to shorter timescales, will have a broad range of potential applications, from infrastructure stability to volcanic eruptions

Predictable Internal Brain Dynamics in EEG and Its Relation to Conscious States

Consciousness is a complex and multi-faceted phenomenon defying scientific explanation. Part of the reason why this is the case is due to its subjective nature. In our previous computational experiments, to avoid such a subjective trap, we took a strategy to investigate objective necessary conditions of consciousness. Our basic hypothesis was that predictive internal dynamics serves as such a condition. This is in line with theories of consciousness that treat retention (memory), protention (anticipation), and primary impression as the tripartite temporal structure of consciousness. To test our hypothesis, we analyzed publicly available sleep and awake electroencephalogram (EEG) data. Our results show that EEG signals from awake or rapid eye movement (REM) sleep states have more predictable dynamics compared to those from slow-wave sleep (SWS). Since awakeness and REM sleep are associated with conscious states and SWS with unconscious or less consciousness states, these results support our hypothesis. The results suggest an intricate relationship among prediction, consciousness, and time, with potential applications to time perception and neurorobotics.The open access fee for this work was funded through the Texas A&M University Open Access to Knowledge (OAK) Fund

Estudio del toolbox “Parallel Computing” de MATLAB®. Aplicación a métodos de procesado de señal

El procesado de señales biomédicas se ha convertido en los últimos años en una de las ramas más destacadas de la Ingeniería Biomédica, que ha permitido evolucionar desde una evaluación visual de las características de las señales biomédicas hasta el uso de sistemas de análisis y procesado avanzados en la actualidad. Dicho avance ha propiciado que los requisitos computacionales sean cada vez más exigentes, desembocando en una mayor cantidad de datos generados para su posterior procesamiento y análisis. Este aumento de información ha causado que su tiempo de procesamiento aumente de forma considerable y que cada vez se requieran de sistemas más potentes. La computación paralela permite solventar parcialmente este problema. Básicamente, consiste en dividir una tarea compleja en varias subtareas para que sean ejecutadas por lotes al mismo tiempo, repartiéndolas entre los procesadores disponibles. Con ello, no solo se intenta reducir el tiempo de realización de la tarea inicial, sino que también se intenta aportar escalabilidad al problema. En este Trabajo Fin de Grado (TFG) se han aplicado técnicas de computación paralela a dos programas en MATLAB® desarrollados por el Grupo de Ingeniería Biomédica de la Universidad de Valladolid con una gran carga computacional, para intentar reducirla y obtener un coste de ejecución más aceptable. Entre las distintas estrategias utilizadas, destacaron la vectorización y los bucles paralelos, que conseguían reducir hasta en un 65% la carga computacional. Ambas estrategias destacan por su facilidad de uso y su alta eficacia.Grado en Ingeniería de Tecnologías Específicas de Telecomunicació

Causally Investigating Cortical Dynamics and Signal Processing by Targeting Natural System Attractors With Precisely Timed (Electrical) Stimulation

Electrical stimulation is a promising tool for interacting with neuronal dynamics to identify neural mechanisms that underlie cognitive function. Since effects of a single short stimulation pulse typically vary greatly and depend on the current network state, many experimental paradigms have rather resorted to continuous or periodic stimulation in order to establish and maintain a desired effect. However, such an approach explicitly leads to forced and “unnatural” brain activity. Further, continuous stimulation can make it hard to parse the recorded activity and separate neural signal from stimulation artifacts. In this study we propose an alternate strategy: by monitoring a system in realtime, we use the existing preferred states or attractors of the network and apply short and precise pulses in order to switch between those states. When pushed into one of its attractors, one can use the natural tendency of the system to remain in such a state to prolong the effect of a stimulation pulse, opening a larger window of opportunity to observe the consequences on cognitive processing. To elaborate on this idea, we consider flexible information routing in the visual cortex as a prototypical example. When processing a stimulus, neural populations in the visual cortex have been found to engage in synchronized gamma activity. In this context, selective signal routing is achieved by changing the relative phase between oscillatory activity in sending and receiving populations (communication through coherence, CTC). In order to explore how perturbations interact with CTC, we investigate a network of interneuronal gamma (ING) oscillators composed of integrate-and-fire neurons exhibiting similar synchronization and signal routing phenomena. We develop a closed-loop stimulation paradigm based on the phase-response characteristics of the network and demonstrate its ability to establish desired synchronization states. By measuring information content throughout the model, we evaluate the effect of signal contamination caused by the stimulation in relation to the magnitude of the injected pulses and intrinsic noise in the system. Finally, we demonstrate that, up to a critical noise level, precisely timed perturbations can be used to artificially induce the effect of attention by selectively routing visual signals to higher cortical areas

Técnicas de conectividad cerebral y transferencia de información aplicado al estudio de la esquizofrenia

Santiago Ramón y Cajal demostró que el sistema nervioso y el cerebro estaban formados por células, al igual que el resto de los tejidos vivos. Esas células fueron llamadas neuronas y las conexiones entre ellas, la sinapsis, son fundamentales para su funcionamiento y comunicación. Todo lo que ocurre dentro del cerebro puede describirse como un entramado de corrientes eléctricas y reacciones bioquímicas entre neuronas. A partir del siglo XIX, la observación y estudio de síndromes o enfermedades debidas a lesiones cerebrales jugó un papel trascendental en el desarrollo de las neurociencias. Por primera vez fue posible establecer algunas correlaciones entre determinadas áreas del cerebro y determinadas funciones mentales superiores como el lenguaje o la memoria. Sin embargo, hace tiempo que se ha superado ese modelo localizacionista. Hoy se asume que las funciones cognitivas no están localizadas en un área cerebral específica, sino que se basan en el funcionamiento de complejos sistemas funcionales. Gracias a las técnicas de neuroimagen funcional, podemos relacionar una tarea concreta con un determinado patrón de activación cerebral, es decir, un conjunto de áreas coactivadas. Uno de los grandes retos de la neurociencia en la actualidad es consolidar el conocimiento de los patrones de actividad cerebral. Por otro lado, muchas patologías tanto neurológicas como psiquiátricas, no obedecen a una lesión focal o a la alteración de una sola área cerebral. Diferentes trastornos, como la esquizofrenia o el autismo, se entienden en la actualidad como desordenes complejos de la conectividad neural. El estudio de la conectividad neural "in vivo" es uno de los principales objetivos de las técnicas de neuroimagen. Un objetivo en un futuro próximo es que estas técnicas, que se centran en el estudio de la conectividad entre distintas áreas cerebrales, permitan mejorar de forma directa los diagnósticos y de forma indirecta los tratamientos de diversas patologías neurológicas y mentalesDe La Iglesia Vayá, MDLD. (2011). Técnicas de conectividad cerebral y transferencia de información aplicado al estudio de la esquizofrenia [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10987Palanci