https://repositorio.fleni.org.ar/xmlui/handle/123456789/13 2026-04-05T17:51:21Z https://repositorio.fleni.org.ar/xmlui/handle/123456789/1458 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 https://repositorio.fleni.org.ar/xmlui/handle/123456789/978 Development of an online calculator for the prediction of seizure freedom following pediatric hemispherectomy using the Hemispherectomy Outcome Prediction Scale (HOPS) Weil, Alexander G.; Dimentberg, Evan; Lewis, Evan C.; Ibrahim, George M.; Kola, Olivia; Tseng, Chi-Hong; Chen, Jia-Shu; Lin, Kao-Min; Cai, Li-Xin; Liu, Qing-Zhu; Lin, Jiu-Luan; Zhou, Wen-Jing; Mathern, Gary W.; Smyth, Matthew D.; O'Neill, Brent R.; Dudley, Roy; Ragheb, John; Pociecha, Juan; Chamorro, Noelia; Muro, Valeria L. Objectives: Although hemispheric surgeries are among the most effective procedures for drug-resistant epilepsy (DRE) in the pediatric population, there is a large variability in seizure outcomes at the group level. A recently developed HOPS score provides individualized estimation of likelihood of seizure freedom to complement clinical judgement. The objective of this study was to develop a freely accessible online calculator that accurately predicts the probability of seizure freedom for any patient at 1-, 2-, and 5-years post-hemispherectomy. Methods: Retrospective data of all pediatric patients with DRE and seizure outcome data from the original Hemispherectomy Outcome Prediction Scale (HOPS) study were included. The primary outcome of interest was time-to-seizure recurrence. A multivariate Cox proportional-hazards regression model was developed to predict the likelihood of post-hemispheric surgery seizure freedom at three time points (1-, 2- and 5- years) based on a combination of variables identified by clinical judgment and inferential statistics predictive of the primary outcome. The final model from this study was encoded in a publicly accessible online calculator on the International Network for Epilepsy Surgery and Treatment (iNEST) website (https://hops-calculator.com/). Results: The selected variables for inclusion in the final model included the five original HOPS variables (age at seizure onset, etiologic substrate, seizure semiology, prior non-hemispheric resective surgery, and contralateral fluorodeoxyglucose-positron emission tomography [FDG-PET] hypometabolism) and three additional variables (age at surgery, history of infantile spasms, and magnetic resonance imaging [MRI] lesion). Predictors of shorter time-to-seizure recurrence included younger age at seizure onset, prior resective surgery, generalized seizure semiology, FDG-PET hypometabolism contralateral to the side of surgery, contralateral MRI lesion, non-lesional MRI, non-stroke etiologies, and a history of infantile spasms. The area under the curve (AUC) of the final model was 73.0%. Significance: Online calculators are useful, cost-free tools that can assist physicians in risk estimation and inform joint decision-making processes with patients and families, potentially leading to greater satisfaction. Although the HOPS data was validated in the original analysis, the authors encourage external validation of this new calculator. 2023-06-22T00:00:00Z https://repositorio.fleni.org.ar/xmlui/handle/123456789/626 A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures Quintero Rincón, Antonio; D'Giano, Carlos; Batatia, Hadj The 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. 2019-08-28T00:00:00Z https://repositorio.fleni.org.ar/xmlui/handle/123456789/577 Mu-suppression detection in motor imagery electroencephalographic signals using the generalized extreme value distribution Quintero Rincón, Antonio; D’Giano, Carlos; Batatia, Hadj This 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. 2020-06-19T00:00:00Z