https://repositorio.fleni.org.ar/xmlui/handle/123456789/92 2026-04-05T19:43:45Z 2026-04-05T19:43:45Z Adell, Matias F. Balda, Javier Casas, Facundo D’Giano, Carlos Quintero-Rincón, Antonio https://repositorio.fleni.org.ar/xmlui/handle/123456789/1458 2025-12-02T14:08:57Z 2025-06-01T00:00:00Z

Pipeline to detect spike-and-wave EEG patterns based on polynomial regression modeling and Taylor series feature selection Adell, Matias F.; Balda, Javier; Casas, Facundo; D’Giano, Carlos; Quintero-Rincón, Antonio Epilepsy is a common neurological disorder diagnosed and monitored through EEG recordings. Accurate spike-and-wave (SW) pattern classification is crucial for distinguishing this epileptic seizure disorder from normal brain wave activity (NW). However, mathematically modeling SW remains challenging, affecting classification accuracy. This study proposes a pipeline in two stages combining polynomial regression techniques, and data processing, in a machine-learning classification scheme. At the first stage of decision-making, the idea is to create a generalized waveform mother that represents all the waveforms of the EEG patterns, such as SW and NW. This waveform is derived from a polynomial regression model that is assessed by the truncation error of the Taylor series. In the second stage, a feature selection algorithm based on a vector that includes the coefficients from Taylor and the statistical properties of the SW and NW waveforms was designed for the machine learning classifier. This algorithm uses the confidence interval to extract the Taylor series points that do not represent the generalized mother equation. This yields a dimensional reduction of this vector, which can be used in a classification and detection scheme. Three polynomial regression models, such as Fourier, Gaussian, and sums-of-sines were evaluated using the pipeline methodology. The best model was the Fourier regression, which achieved an accuracy of 96.2% using the SVM classifier with a Gaussian kernel to detect spike-and-wave patterns.

2025-06-01T00:00:00Z Quintero Rincón, Antonio D'Giano, Carlos Batatia, Hadj https://repositorio.fleni.org.ar/xmlui/handle/123456789/464 2024-01-22T13:45:09Z 2021-01-01T00:00:00Z

Artifacts Detection in EEG Signals Quintero Rincón, Antonio; D'Giano, Carlos; Batatia, Hadj Resumen no disponible

2021-01-01T00:00:00Z Quintero Rincón, Antonio D'Giano, Carlos Batatia, Hadj https://repositorio.fleni.org.ar/xmlui/handle/123456789/93 2024-01-22T13:47:26Z 2019-07-11T00:00:00Z

Seizure Onset Detection in EEG Signals Based on Entropy from Generalized Gaussian PDF Modeling and Ensemble Bagging Classifier Quintero Rincón, Antonio; D'Giano, Carlos; Batatia, Hadj This paper proposes a new algorithm for epileptic seizure onset detection in EEG signals. The algorithm relies on the measure of the entropy of observed data sequences. Precisely, the data is decomposed into different brain rhythms using wavelet multi-scale transformation. The resulting coefficients are represented using their generalized Gaussian distribution. The proposed algorithm estimates the parameters of the distribution and the associated entropy. Next, an ensemble bagging classifier is used to performs the seizure onset detection using the entropy of each brain rhythm, by discriminating between seizure and non-seizure. Preliminary experiments with 105 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.

2019-07-11T00:00:00Z