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

Artefacts Detection in EEG Signals

Chapter 11 demonstrates the potential of artefacts detection approach in electro- encephalography, using the Hampel filter to correct different types of artefacts. Also, a complete state-of-the-art is introduced along with a recommended bibliography to research these topics

Epilepsy seizure onset detection applying 1-NN classifier based on statistical parameters

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 signal

Epileptic seizure prediction using Pearson's product-moment correlation coefficient of a linear classifier from generalized Gaussian modeling

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 señales 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 señales 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 señales epilépticas.To predict an epileptic event, means the ability to determine in advance the time of the seizure with the highest possible accuracy. A correct prediction benchmark for epilepsy events in clinical applications, is a typical problem in biomedical signal processing that help to an appropriate diagnosis and treatment of this disease. In this work we use Pearson's product-moment correlation coefficient from generalized Gaussian distribution parameters coupled with linear-based classifier to predict between seizure and non-seizure events in epileptic EEG signals. The performance in 36 epileptic events from 9 patients showing a good performance with 100% of effectiveness for sensitivity and specificity greater than 83% for seizures events in all brain rhythms. Pearson's test suggest that all brain rhythms are highly correlated in non-seizure events but no during the seizure events. This suggests that our model can be scaled with the Pearson product-moment correlation coefficient for the detection of epileptic seizures.Fil: Quintero Rincón, Antonio. Instituto Tecnológico de Buenos Aires; ArgentinaFil: D'Giano, Carlos. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Risk, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Tecnológico de Buenos Aires; Argentin

A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures

International audienceThe two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals. In this paper, we develop a model-based classification method to detect epileptic seizures that relies on this algorithm to filter electroencephalogram (EEG) signals. The underlying idea was to design an EEG filter that enhances the waveform of epileptic signals. The filtered signal was fitted to a quadratic linear-parabolic model using the curve fitting technique. The model fitting was assessed using four statistical parameters, which were used as classification features with a random forest algorithm to discriminate seizure and non-seizure events. The proposed method was applied to 66 epochs from the Children Hospital Boston database. Results showed that the method achieved fast and accurate detection of epileptic seizures, with a 92% sensitivity, 96% specificity, and 94.1% accuracy

Study on spike-and-wave detection in epileptic signals using t-location-scale distribution and the k-nearest neighbors classifier

International audiencePattern 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