1,720,994 research outputs found
Artifacts Detection in EEG Signals
No full textResumen no disponibleFil: Quintero-Rincón, Antonio. Instituto Tecnológico de Buenos Aires. Departamento de Bioingeniería; Argentina.Fil: D'Giano, Carlos. Fleni. Centro Integral de Epilepsia y Unidad de Monitoreo de Videoelectroencefalografía; Argentina.Fil: Batatia, Hadj. University of Toulouse. Institut de Recherche en Informatique de Toulouse; Francia
Mu-suppression detection in motor imagery electroencephalographic signals using the generalized extreme value distribution
No full textThis paper deals with the detection of mu-suppression from electroencephalographic (EEG) signals in brain-computer interface (BCI). For this purpose, an efficient algorithm is proposed based on a statistical model and a linear classifier. Precisely, the generalized extreme value distribution (GEV) is proposed to represent the power spectrum density of the EEG signal in the central motor cortex. The associated three parameters are estimated using the maximum likelihood method. Based on these parameters, a simple and efficient linear classifier was designed to classify three types of events: imagery, movement, and resting. Preliminary results show that the proposed statistical model can be used in order to detect precisely the mu-suppression and distinguish different EEG events, with very
good classification accuracy.Fil: D’Giano, Carlos. Fleni. Centro Integral de Epilepsia y Unidad de Monitoreo de Videoelectroencefalografía; Argentina.Fil: Quintero-Rincón, Antonio. Instituto Tecnológico de Buenos Aires. Departamento de Bioingeniería; Argentina.Fil: Batatia, Hadj. University of Toulouse; Francia
Epileptic seizure prediction using Pearson's product-moment correlation coefficient of a linear classifier from generalized Gaussian modeling
Full text linkPredecir 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
Study on epileptic seizure detection in EEG signals using largest Lyapunov exponents and logistic regression
No full textSeizure 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 specificityFil: Quintero-Rincón, Antonio. Fleni; Argentina.Fil: Flugelman, Máximo. Instituto Tecnológico de Buenos Aires. Departamento de Bioingeniería; Argentina.Fil: Prendes, Jorge. University of Toulouse, Institut de Recherche en Informatique de Toulouse; Francia.Fil: D'Giano, Jorge. Fleni. Centro Integral de Epilepsia y Unidad de Monitoreo de Videoelectroencefalografía; Argentina
Hand bone conduction sound study by using the DSP Logger MX 300
No full textBone conduction is the transmission of acoustic energy to the inner ear by different paths involving the bones of the skull. In this work, we use the path the hand provides in order to transmit the sound coming from the cell phone using Bluetooth system. The aim of this work was to study the vibrations produced by a sound transmitted through bone conduction between a mobile phone and the hand analyzed with the DSP Logger MX equipment.Fil: Adler, Melanie Victoria. Instituto Tecnológico de Buenos Aires; Argentina.Fil: Fialá Sánchez, Mariana. Instituto Tecnológico de Buenos Aires; Argentina.Fil: Martini, Constanza. Instituto Tecnológico de Buenos Aires; Argentina.Fil: Vartabedian, Luciana Mariam. Instituto Tecnológico de Buenos Aires; Argentina.Fil: Zazzali, Matías Nicolás. Instituto Tecnológico de Buenos Aires; Argentina.Fil: Quintero-Rincón, Antonio. Fleni; Argentina
Epilepsy seizure onset detection applying 1-NN classifier based on statistical parameters
No full textEpilepsy 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.Fil: Zorgno, Ivanna. Instituto Tecnológico de Buenos Aires. Departamento de Bioingeniería; Argentina.Fil: Blanc, María Cecilia. Instituto Tecnológico de Buenos Aires. Departamento de Bioingeniería; Argentina.Fil: Oxenford, Simon. Instituto Tecnológico de Buenos Aires. Departamento de Bioingeniería; Argentina.Fil: Gil Garbagnoli, Francisco. Instituto Tecnológico de Buenos Aires. Departamento de Bioingeniería; Argentina.Fil: D’Giano, Carlos. Fleni. Centro Integral de Epilepsia y Unidad de Monitoreo de Videoelectroencefalografía; Argentina.Fil: Quintero-Rincón, Antonio. Instituto Tecnológico de Buenos Aires. Departamento de Bioingeniería; Argentina
Spike-and-wave epileptiform discharge pattern detection based on Kendall's Tau-b coefficient
Full text linkEpilepsy is an important public health issue. An appropriate epileptiform discharge 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-b 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.Fil: Quintero-Rincón, Antonio. Instituto Tecnológico de Buenos Aires. Departamento de Bioingeniería; Argentina.Fil: Carenzo, Catalina. Instituto Tecnológico de Buenos Aires. Departamento de Bioingeniería; Argentina.Fil: Ems, Joaquín. Instituto Tecnológico de Buenos Aires. Departamento de Bioingeniería; Argentina.Fil: Hirschson, Lourdes. Instituto Tecnológico de Buenos Aires. Departamento de Bioingeniería; Argentina.Fil: Muro, Valeria L. Fleni. Centro Integral de Epilepsia y Unidad de Monitoreo de Videoelectroencefalografía; Argentina.Fil: D'Giano, Carlos. Fleni. Centro Integral de Epilepsia y Unidad de Monitoreo de Videoelectroencefalografía; Argentina
A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
Full text linkThis 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.Fil: Quintero Rincón, Antonio. Instituto Tecnológico de Buenos Aires; ArgentinaFil: Pereyra, M.. University Of Bristol; Reino UnidoFil: D'Giano, Carlos. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Batatia, H.. Universite de Toulouse; FranciaFil: Risk, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Tecnológico de Buenos Aires; Argentin
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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