Estimation of Action Potential of the Cellular Membrane using Support Vectors Machines

Seijas, César; Caralli, Antonino; Villazana, Sergio

doi:10.1109/iembs.2006.260149

Cited by 8 publications

(5 citation statements)

References 2 publications

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“…Figure 2 shows the spectral response corresponding to the EEG channel record with higher energy under analysis (channel O1), note the 10 Hz spectral component, that exhibits a maximum peak power (≈+7.12 dB), which is an indicative of brain activity  corticothalamic of the patient; as well as the strong rejection of common mode line noise of approximately −44 dB, below the highest energy components. 4) Support vector machines based classifier was implemented (SVC [11][12][13]) and was trained and validated, with the matrices listed above. In the developed application in this paper, the best performance, as a function kernel, was exhibited by radial basis function after testing different kernel functions tuning their parameters.…”

Section: Methodology and Analysis Of Resultsmentioning

confidence: 99%

Neuropathology Classifier Based on Higher Order Spectra

Seijas¹,

Caralli²,

Villazana³

2013

JCC

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Epilepsy is the most common neuropathology. Statistical studies related to the disease reported that 20%-25% of epileptic patients with occurrence of seizures were even under treatment with drugs. This article presents a strategy for improved detection of the neuropathology, based on electroencephalogram (EEG), using a classifier built with support vector machines (SVC). The SVC is designed based on feature extraction of higher order spectra of time series derived from the EEG applied to epileptic patients and control patients. As demonstrated in the study presented, the EEG time series are highly nonlinear and non-Gaussian, therefore, exhibit higher order spectra, which are extracted features that improve the accuracy in the performance of SVC. The results of this study suggest the development of highly accurate computational tools for the diagnosis of this dreaded neuropathology.

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Section: Methodology and Analysis Of Resultsmentioning

confidence: 99%

Neuropathology Classifier Based on Higher Order Spectra

Seijas¹,

Caralli²,

Villazana³

2013

JCC

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“…Twenty individuals were randomly selected from each group, and their heart rates were collected and subsequently categorized using an SVM. Finally, [19] discusses the use of this algorithm in biochemical applications. In this study, a SVM was used to estimate the action potential of the cell membrane.…”

Section: Naïve Bayes (Nb)mentioning

confidence: 99%

Supervised Learning - A Systematic Literature Review

Dridi

2024

Preprint

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Machine Learning (ML) is a rapidly emerging field that enables a plethora of innovative approaches to solving real-worldproblems. It enables machines to learn without human intervention from data and is used in a variety of applications,from fraud detection to recommendation systems and medical imaging. Supervised learning, unsupervised learning, andreinforcement learning are the 3 main categories of ML. Supervised learning involves pre-training the model on a labeleddataset and entails two distinct types of learning: classification and regression. Regression is used when the output iscontinuous. By contrast, classification is used when the output is categorical.Supervised learning aims to optimize class label models using predictor features. Following that, a second classifieris used to assign class labels to the test data in cases where the values of the predictor characteristics are known butthe value of the class label is unknown. In classification, the label identifies the class to which the training set belongs.However, in regression, the label is a real-value response that corresponds to the example.

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“…Para cada uno de los EEG de la base de datos, se aplicó filtrado digital pasabandas 0,1-100 Hz) y de línea (60 Hz); así como también, se eliminaron segmentos, que, a simple inspección visual, lucieron afectados por artefactos técnicos, tales como desplazamiento accidental de electrodos [2,4]. En cada EEG, ya filtrado, se identificó el canal que exhibió mayor potencia, usando la respuesta espectral, mediante EEGLAB (Figuras 5a y 5b).…”

Section: Procesamiento Y Selección Del Canalunclassified

“…El Electroencefalograma (EEG) es el registro de las señales de la actividad electrofisiológica de las células cerebrales, conocidas como neuronas. Esta actividad es el resultado de la superposición de múltiples impulsos eléctricos (potencial de acción) [4,1], generados en las neuronas, producto del intercambio iónico a través de la membrana celular (proceso de difusión iónica). El EEG se obtiene mediante la colocación de un conjunto de electrodos en el cráneo, sobre la región del cuero cabelludo.…”

Section: Introductionunclassified

Detector de Neuropatologías en EEG usando Estadísticas de Orden Superior y Aprendizaje Profundo

et al. 2021

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En el presente artículo se presenta un detector de neuropatologías, a partir del electroencefalograma (EEG) del paciente. La detección se basa en la clasificación de imágenes de HOSA (siglas en inglés para análisis de estadísticas de orden superior o “High Order Statistical Analysis”) derivadas de series de tiempo correspondientes a EEG de pacientes humanos. El clasificador es un modelo de aprendizaje profundo DL (“Deep Learning”) con la arquitectura de la CNN (Red Neuronal Convolucional o “Convolutional Neural Networks”) pre-entrenada: “Inception”. El conjunto de entrenamiento y prueba de la CNN son imágenes de HOSA, que representan los cumulantes de tercer orden de segmentos no lineales y no gaussianos, de señales correspondientes al canal seleccionado del EEG de pacientes con neuropatologías (específicamente, epilepsia) o sanos. El desempeño del clasificador es muy satisfactorio, presentando una exactitud de aproximadamente 94 % en la detección de epilepsia.

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Estimation of Action Potential of the Cellular Membrane using Support Vectors Machines

Cited by 8 publications

References 2 publications

Neuropathology Classifier Based on Higher Order Spectra

Neuropathology Classifier Based on Higher Order Spectra

Supervised Learning - A Systematic Literature Review

Detector de Neuropatologías en EEG usando Estadísticas de Orden Superior y Aprendizaje Profundo

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