RI ITBA :: Examinando porby browse.metadata.subject "ELECTROENCEFALOGRAFIA"

Examinando por Materia "ELECTROENCEFALOGRAFIA"

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Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier
(2017-02) Redelico, Francisco; Traversaro Varela, Francisco; García, María del Carmen; Silva, Walter; Rosso, Osvaldo A.; Risk, Marcelo
"In this contribution, a comparison between different permutation entropies as classifiers of electroencephalogram (EEG) records corresponding to normal and pre-ictal states is made. A discrete probability distribution function derived from symbolization techniques applied to the EEG signal is used to calculate the Tsallis entropy, Shannon Entropy, Renyi Entropy, and Min Entropy, and they are used separately as the only independent variable in a logistic regression model in order to evaluate its capacity as a classification variable in a inferential manner. The area under the Receiver Operating Characteristic (ROC) curve, along with the accuracy, sensitivity, and specificity are used to compare the models. All the permutation entropies are excellent classifiers, with an accuracy greater than 94.5% in every case, and a sensitivity greater than 97%. Accounting for the amplitude in the symbolization technique retains more information of the signal than its counterparts, and it could be a good candidate for automatic classification of EEG signals."
Confidence intervals and hypothesis testing for the Permutation Entropy with an application to epilepsy
(2018-04) Traversaro Varela, Francisco; Redelico, Francisco
"In nonlinear dynamics, and to a lesser extent in other fields, a widely used measure of complexity is the Permutation Entropy. But there is still no known method to determine the accuracy of this measure. There has been little research on the statistical properties of this quantity that characterize time series. The literature describes some resampling methods of quantities used in nonlinear dynamics - as the largest Lyapunov exponent - but these seems to fail. In this contribution, we propose a parametric bootstrap methodology using a symbolic representation of the time series to obtain the distribution of the Permutation Entropy estimator. We perform several time series simulations given by well-known stochastic processes: the 1/f α noise family, and show in each case that the proposed accuracy measure is as efficient as the one obtained by the frequentist approach of repeating the experiment. The complexity of brain electrical activity, measured by the Permutation Entropy, has been extensively used in epilepsy research for detection in dynamical changes in electroencephalogram (EEG) signal with no consideration of the variability of this complexity measure. An application of the parametric bootstrap methodology is used to compare normal and pre-ictal EEG signals."
Design of ESD protections for ECG applications
(2020-02) Gardella, Pablo; Baez, Eduardo; Cesaretti, Juan Manuel
"This paper presents the design of Electrostatic Discharge (ESD) protections for a remote Electroencephalograph (ECG). Design and layout guidelines are analyzed to improve the ESD robustness of a Grounded-Gate NMOS (GGNMOS) cell based on a single well CMOS-only process. Experimental validation is done by means of a Time Domain Reflectometry (TDR) technique known as Transmission Line Pulse (TLP) testing. The silicon implementation of the proposed design passes ±3700V in the Human-Body Model (HBM)."
Differential neurophysiological correlates of retrieval of consolidated and reconsolidated memories in humans: an ERP and pupillometry study
(2020-10) Campos-Arteaga, G.; Forcato, Cecilia; Wainstein, G.; Lagos, R.; Palacios-García, I.; Artigas, C.; Morales, R.; Pedreira, María Eugenia; Rodríguez, E.
"Consolidated memories can return to a labile state if they are reactivated by unpredictable reminders. To persist, active memories must be re-stabilized through a process known as reconsolidation. Although there is consistent behavioral evidence about this process in humans, the retrieval process of reconsolidated memories remains poorly understood. In this context, one fundamental question is whether the same or different neurophysiological mechanisms are involved in retrieval of consolidated and reconsolidated memories. Because it has been demonstrated that the exposure to the reconsolidation process may restructure and strengthen memories, we hypothesized distinct neurophysiological patterns during retrieval of reconsolidated memories. In addition, we hypothesized that interfering with the reconsolidation process using a new learning can prevent these neurophysiological changes. To test it, consolidated, reconsolidated and declarative memories whose reconsolidation process was interfered (i.e., picture-word pairs) were evaluated in humans in an old/new associative recall task while the brain activity and the pupillary response were recorded using electroencephalography and eyetracking. Our results showed that retrieval of reconsolidated memories elicits specific patterns of brain activation, characterized by an earlier peak latency and a smaller magnitude of the left parietal ERP old/new effect compared to memories that were only consolidated or whose reconsolidation process was interfered by a new learning. Moreover, our results demonstrated that only retrieval of reconsolidated memories is associated with a late reversed mid-frontal effect in a 600–690 time window. Complementarily, memories that were reactivated showed an earlier peak latency of the pupil old/new effect compared to non-reactivated memories. These findings support the idea that reconsolidation has an important impact in how memories are retrieved in the future, showing that retrieval of reconsolidated memories is partially supported by specific brain mechanisms."
EEG waveform analysis of P300 ERP with applications to brain computer interfaces
(2018-11) Ramele, Rodrigo; Villar, Ana Julia; Santos, Juan Miguel
"The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition."
EEG waveform identification based on deep learning techniques
(2022) Ail, Brian Ezequiel; Ramele, Rodrigo
"The use of Brain-Computer Interfaces can provide substantial improvements to the quality of life of patients with diseases such as severe Amyotrophic lateral sclerosis that cause Locked-in syndrome, by creating new avenues in which these people can communicate and interact with the outside world. The P300 speller is an interface which provide the patients the ability to spell letters and eventually words, so that they can speak while unable to use their mouth. The P300 speller works by reading signals from the brain using an Electroencephalogram. Traditionally, these signals were plotted and interpreted by specialized technicians or neurologists, but the development of Machine learning algorithms for classification allow the computers to perform this analysis and detect the P300 signals, which is an Event Related Potential triggered when certain stimuli such as a bright light is triggered on a place that the patient is focused on. In this thesis we used a Convolutional Neural Network to train multi-channel EEG readings, and attempted to detect P300 signals from a P300 speller. The results are corroborated against a public ALS dataset."
Epilepsy seizure onset detection applying 1-NN classifier based on statistical parameters
(2018) Zorgno, Ivanna; Blanc, María Cecilia; Oxenford, Simón; Gil Garbagnoli, Francisco; D'Giano, Carlos; Quintero-Rincón, Antonio
"Epilepsy is a disease caused by an excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an ongoing challenge in biomedical signal processing. In this paper, a new method is proposed for onset seizure detection in epileptic EEG signals based on parameters from the t-location-scale distribution coupled with the variance and the Pearson correlation coefficient. The 1-nearest neighbor classifier achieved a 91% sensitivity (True positive rate) and 95% specificity (True Negative Rate) with a delay of 4.5 seconds (on average) in the 45 signals analyzed, which suggests that the proposed methodology is potentially useful for seizure onset detection in epileptic EEG signals."
Epilepsy seizure onset detection applying 1-NN classiﬁer based on statistical parameters
(2018) Zorgno, Ivanna; Blanc, María Cecilia; Oxenford, Simón; Gil Garbagnoli, Francisco; D'Giano, Carlos; Quintero-Rincón, Antonio
"Epilepsy is a disease caused by an excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an ongoing challenge in biomedical signal processing. In this paper, a new method is proposed for onset seizure detection in epileptic EEG signals based on parameters from the t-location-scale distribution coupled with the variance and the Pearson correlation coefﬁcient. The 1-nearest neighbor classiﬁer achieved a 91% sensitivity (True positive rate) and 95% speciﬁcity (True Negative Rate) with a delay of 4.5 seconds (on average) in the 45 signals analyzed, which suggests that the proposed methodology is potentially useful for seizure onset detection in epileptic EEG signals."
Experimental setup to test neurological artifact elimination techniques
(2021-03) Tobar, Magdalena; Ramele, Rodrigo
"This work proposes a methodological approach to the insertion of a particular artifact produced by eyes blinking, in EEG data, in order to create a pseudo-real dataset, which in turn provides an experimental setup to test artifact removal algorithms. The topic of ocular waves’ propagation throughout EEG channels is also covered. The optimal amount of ICA components is discovered through an iterative approach, which includes an observation step."
Fast statistical model-based classification of epileptic EEG signals
(2018-01) Quintero-Rincón, Antonio; Pereyra, Marcelo; D'Giano, Carlos; Risk, Marcelo; Batatia, Hadj
"This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computational cost, making it suitable for real-time implementation in EEG devices; it performs detection separately for each brain rhythm or EEG spectral band, following the current medical practices; and it can be trained with small datasets, which is key in clinical problems where there is limited annotated data available. This is in sharp contrast with modern approaches based on machine learning techniques, which achieve very high sensitivity and specificity but require large training sets with expert annotations that may not be available. The proposed method proceeds by first separating EEG signals into their five brain rhythms by using awavelet filter bank. Each brain rhythm signal is then mapped to a low-dimensional manifold by using a generalized Gaussian statistical model; this dimensionality reduction step is computationally straightforward and greatly improves supervised classification performance in problems with little training data available. Finally, this is followed by parallel linear classifications on the statistical manifold to detect if the signals exhibit healthy or abnormal brain activity in each spectral band. The good performance of the proposed method is demonstrated with an application to paediatric neurology using 39 EEG recordings from the Children's Hospital Boston database, where it achieves an average sensitivity of 98%, specificity of 88%, and detection latency of 4 s, performing similarly to the best approaches from the literature."
Histogram of gradient orientations of EEG signal plots for brain computer interfaces
(2018) Ramele, Rodrigo; Santos, Juan Miguel; Villar, Ana Julia
"Brain Computer Interface (BCI) or Brain Machine Interfaces (BMI), has proved the feasibility of a distinct non-biological communication channel to transmit information from the Central Nervous System (CNS) to a computer device. Promising success has been achieved with invasive BCI, though biocompatibilities issues and the complexity and risks of surgical procedures are the main drive to enhance current non-invasive technologies. Electroencephalography (EEG) is the most widespread method to gather information from the CNS in a non-invasive way. Clinical EEG has traditionally focused on temporal waveforms, but signal analysis methods which follow this path have been neglected in BCI research. This Thesis proposes a method and framework to analyze the waveform, the shape of the EEG signal, using the histogram of gradient orientations, a fruitful technique from Computer Vision which is used to characterize image local features. Inspiration comes from what traditionally electroencephalographers have been doing for almost a century: visually inspecting raw EEG signal plots."
Histogram of gradient orientations of signal plots applied to P300 detection
(2019-07) Ramele, Rodrigo; Villar, Ana Julia; Santos, Juan Miguel
"The analysis of Electroencephalographic (EEG) signals is of ulterior importance to aid in the diagnosis of mental disease and to increase our understanding of the brain. Traditionally, clinical EEG has been analyzed in terms of temporal waveforms, looking at rhythms in spontaneous activity, subjectively identifying troughs and peaks in Event-Related Potentials (ERP), or by studying graphoelements in pathological sleep stages. Additionally, the discipline of Brain Computer Interfaces (BCI) requires new methods to decode patterns from non-invasive EEG signals. This field is developing alternative communication pathways to transmit volitional information from the Central Nervous System. The technology could potentially enhance the quality of life of patients affected by neurodegenerative disorders and other mental illness. This work mimics what electroencephalographers have been doing clinically, visually inspecting, and categorizing phenomena within the EEG by the extraction of features from images of signal plots. These features are constructed based on the calculation of histograms of oriented gradients from pixels around the signal plot. It aims to provide a new objective framework to analyze, characterize and classify EEG signal waveforms. The feasibility of the method is outlined by detecting the P300, an ERP elicited by the oddball paradigm of rare events, and implementing an offline P300-based BCI Speller. The validity of the proposal is shown by offline processing a public dataset of Amyotrophic Lateral Sclerosis (ALS) patients and an own dataset of healthy subjects."
A new algorithm for automatic identiﬁcation of spike-wave EEG signals in epileptic patient-speciﬁc
(2018) Racca, Dora María; Quintero-Rincón, Antonio; Muro, Valeria; D'Giano, Carlos
"Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which results 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 processing. In this work we propose a new method to indentify and characterize patient specific spike-and-wave EEG epileptic signals. The method is based on the use of trained neuronal networks on probability density function parameters of the translation and rescaling of the Student'st-distribution (location: µ,scale: σ and shape: ν) of pure spike-and-wave-signals. The neuronal network was trained with both normal and epileptic signals. The study resulted in 100% specificity and sensitivity on the studied signals."
Outliers resistant methods for motor imagery classification
(2018-08) Villar, Ana Julia
"Common spatial patterns analysis (CSP) and linear discriminant analysis (LDA) are widely used techniques for spatial filtering and classifying in motor imagery (MI). However, CSP is very sensitive to noise and artifacts. A method to detect and eliminate anomalous electroencephalogram (EGG) signals before applying CSP is presented. An outlier score of the signal is obtained by calculating the similarities with the other signals of the sample through the Bounded Coordinate System (BCS). Besides, it is proposed to replace the usual estimators of mean, covariance and scale, used in the algorithms, by Olive and Hawkins estimators to get robust versions of BCS and CSP. The assumption done in LDA that the covariance of each of the classes in MI are identical may not be true; if it is not satisfied, it is better to use quadratic discrimination. Tests to verify this hypothesis and decide which discriminant function must be used are considered. The performances of the methods are evaluated and compared on EGG data from BCI competition datasets; results show that robust methods outperformed classical techniques, especially for subjects with poor classification accuracy."
Predicción de crisis epilépticas utilizando el coeficiente de correlación producto-momento de Pearson a partir de un clasificador lineal de la distribución Gaussiana generalizada
(2018) Quintero-Rincón, Antonio; D'Giano, Carlos; Risk, Marcelo
"Predecir una crisis epiléptica significa la capacidad de determinar de antemano el momento de una crisis con la mayor precisión posible. Un pronóstico correcto de un evento epiléptico en aplicaciones clínicas es un problema típico en procesamiento de senales biomédicas, lo cual ayuda a un diagnóstico y tratamiento apropiado de esta enfermedad. En este trabajo, utilizamos el coeficiente de correlación producto-momento de Pearson a partir de las clases estimadas con un clasificador lineal, usando los parámetros de la distribución Gaussiana generalizada. Esto con el fin de poder pronosticar eventos con crisis y eventos con no-crisis en senales epilépticas. El desempeño en 36 eventos epilépticos de 9 pacientes muestra un buen rendimiento, con un 100% de efectividad para sensibilidad y especificidad superior al 83% para eventos con crisis en todos los ritmos cerebrales. El test de Pearson indica que todos los ritmos cerebrales están altamente correlacionados en los eventos con no-crisis, más no durante los eventos con crisis. Esto indica que nuestro modelo puede escalarse con el coeficiente de correlación producto-momento de Pearson para la detección de crisis en senales epilépticas."
Procesamiento de señales de electroencefalograma para detección de crisis epiléptica
(2019) Girado, Sol; Rensonnet, Pablo; Volman, Uriel
"Desarrollo de software de procesamiento de señales de electroencefalograma en Matlab para detección y análisis de crisis epilépticas a partir de estudios de 23 canales."
Slow wave detection algorithm in non-REM sleep
(2020) Carbonari, Giulia; Carosi, Julia; Vázquez Chenlo, Aylin; Moris, Eugenia; Forcato, Cecilia; Ramele, Rodrigo; Larrabide, Ignacio
"Online detection of slow waves."
Spike-and-wave detection in epileptic signals using cross-correlation and decision trees
(2018) Quintero-Rincón, Antonio; Alanis, Manuela; Muro, Valeria; D'Giano, Carlos
"Identify spike-and-waves patterns in epileptic signals is a typical problem in electroencephalographic (EEG) signal processing. In this paper we propose cross-correlation coupled with decision tree model as new method in order to assess and detect spike-and-wave discharges (SWD) in long-term epileptic signals. The proposed approach is demonstrated in terms of accuracy, sensitivity and specificity classification on real EEG signals using a database developed with medical annotations."
Statistical modeling and quantification of EEG signals: application to the characterization and onset detection in epileptic seizures
(2019-04) Quintero-Rincón, Antonio; Risk, Marcelo; Batatia, Hadj
"Identificar la actividad cerebral epiléptica utilizando señales de electroencefalografía (EEG) en tiempo real es un problema difícil. Los métodos modernos de detección basados en técnicas avanzadas de aprendizaje automático, son efectivos pero requieren grandes conjuntos de datos de entrenamiento y son difíciles de implementar en sistemas de monitoreo en tiempo real, debido a su costo computacional relativamente alto. Esta tesis se centra en dos problemas centrales vinculados a la caracterización de las crisis epilépticas con señales de EEG. El primero se relaciona con la detección de inicio y el otro se refiere al reconocimiento de patrones epileptiformes. Usando el nuevo método de caracterización presentado en el capítulo 2, ambos problemas pueden implementarse en tiempo real y lograr un alto rendimiento de detección. En general, esta tesis permitió aportar cinco nuevas contribuciones para tratar los problemas desafiantes de la epilepsia."
Study on epileptic seizure detection in EEG signals using largest Lyapunov exponents and logistic regression
(2019) Quintero-Rincón, Antonio; Flugelman, Máximo; Prendes, Jorge; D'Giano, Carlos
"Seizure detection plays a central role in most aspects of epilepsy care. Understanding the complex epileptic signals system is a typical problem in electroencephalographic (EEG) signal processing. This problem requires different analysis to reveal the underlying behavior of EEG signals. An example of this is the non-linear dynamic: mathematical tools applied to biomedical problems with the purpose of extracting features or quantifying EEG data. In this work, we studied epileptic seizure detection independently in each brain rhythms from a multilevel 1D wavelet decomposition followed by the independent component analysis (ICA) representation of multivariate EEG signals. Next, the largest Lyapunov exponents (LLE) and their scaling given by its standard deviation are estimated in order to obtain the vectors to be used during the training and classification stage. With this information, a logistic regression classification is proposed with the aim of discriminating between seizure and non-seizure. Preliminary experiments with 99 epileptic events suggest that the proposed methodology is a powerful tool for detecting seizures in epileptic signals in terms of classification accuracy, sensitivity and specificity."

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