Browsing by Author "D'Giano, Carlos"
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ponencia en congreso.listelement.badge 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."póster.listelement.badge 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."artículo de publicación periódica.listelement.badge 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."póster.listelement.badge 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."artículo de publicación periódica.listelement.badge 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."artículo de publicación periódica.listelement.badge 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."póster.listelement.badge Spike-and-wave epileptiform discharge pattern detection based on Kendall’s Tau-b Coefficient(2019) Ems, Joaquín; Hirschson Álvarez Prado, Lourdes; Carenzo, Catalina; Muro, Valeria; D'Giano, Carlos; Quintero-Rincón, Antonio"Epilepsy is a main public health issue. An appropriate epileptiform discharges pattern detection of this neurological disease, is a typical problem in biomedical engineering. In this paper, a new method is proposed for spike-and-wave discharge pattern detection based on Kendall’s Tau Coefficient. The proposed approach is demonstrated on a real dataset containing spike-and-wave discharge signals, where our performance is evaluated in terms of high Specificity, rule in (SpPIn) with 94% for patient-specific spike-and-wave discharge detection and 83% for a general spike-and-wave discharge detection."artículo de publicación periódica.listelement.badge 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."ponencia en congreso.listelement.badge Study on spike-and-wave detection in epileptic signals using T-location-scale distribution and the K-nearest neighbors classifier(2017-12) Quintero-Rincón, Antonio; Prendes, Jorge; D'Giano, Carlos; Muro, Valeria"Pattern classification in electroencephalography (EEG) signals is an important problem in biomedical engineering since it enables the detection of brain activity, in particular the early detection of epileptic seizures. In this paper we propose a k-nearest neighbors classification for epileptic EEG signals based on an t-location-scale statistical representation to detect spike-and-waves. The proposed approach is demonstrated on a real dataset containing both spike-and-wave events and normal brain function signals, where our performance is evaluated in terms of classification accuracy, sensitivity and specificity."ponencia en congreso.listelement.badge A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence(2017-10) Quintero-Rincón, Antonio; Pereyra, Marcelo; D'Giano, Carlos; Batatia, Hadj; Risk, Marcelo"This paper presents a statistical signal processing method for the characterization of EEG of patients suffering from epilepsy. A statistical model is proposed for the signals and the Kullback-Leibler divergence is used to study the differences between Seizure/Non-Seizure in patients suffering from epilepsy. Precisely, EEG signals are transformed into multivariate coefficients through multilevel 1D wavelet decomposition of different brain frequencies. The generalized Gaussian distribution (GGD) is shown to model precisely these coefficients. Patients are compared based on the analytical development of Kullback-Leibler divergence (KLD) of their corresponding GGD distributions. The method has been applied to a dataset of 18 epileptic signals of 9 patients. Results show a clear discrepancy between Seizure/Non-Seizure in epileptic signals, which helps in determining the onset of the seizure."