https://repositorio.fleni.org.ar/xmlui/handle/123456789/14 2026-04-05T19:52:33Z 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/123456789/450 Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals Quintero Rincón, Antonio; Muro, Valeria L.; D'Giano, Carlos; Prendes, Jorge; Batatia, Hadj Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) is a crucial signal processing problem in epilepsy applications. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new method to detect SWD, with a low computational complexity making it easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the continuous Morlet 1-D wavelet decomposition is computed. The generalized Gaussian distribution (GGD) is fitted to the resulting coefficients and their variance and median are calculated. Next, a k-nearest neighbors (k-NN) classifier is trained to detect the spike-and-wave patterns, using the scale parameter of the GGD in addition to the variance and the median. Experiments were conducted using EEG signals from six human patients. Precisely, 106 spike-and-wave and 106 non-spike-and-wave signals were used for training, and 96 other segments for testing. The proposed SWD classification method achieved 95% sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These promising results set the path for new research to study the causes underlying the so-called absence epilepsy in long-term EEG recordings. 2020-10-29T00:00:00Z https://repositorio.fleni.org.ar/xmlui/handle/123456789/390 Cytotoxic lesion of the corpus callosum in a patient with aphasic status epilepticus Castiglione, Juan Ignacio; Ricciardi, Mario Emiliano; Bensi, Catalina A 47-year-old man with a history of aphasic seizures presented to the emergency room with a 12-hour global aphasia. Upon admission, brain MRI did not reveal acute lesions, and EEG showed sharp waves in the left frontal-temporal region. An Aphasic Status Epilepticus was diagnosed and antiepileptic treatment was initiated with adequate response. A week after the episode, a new brain MRI showed a high-signal ovoid lesion on T2-weighted and FLAIR sequences in the central part of the splenium of the corpus callosum. On diffusion-weighted images (DWI) the lesion was hyperintense with decreased apparent diffusion coefficient (ADC) values, indicating restricted diffusion consistent with a cytotoxic lesion of the corpus callosum (CLOCC). Follow-up MRI one month later showed complete image resolution. CLOCCs are secondary lesions associated with various entities in which high levels of cytokines and extracellular glutamate cause intracellular edema and reduced diffusion, a condition called cytotoxic edema, which affects vulnerable brain regions such as the splenium of the corpus callosum. In epileptic patients, CLOCCs may be due to the effect of seizures, especially prolonged ones, as well as antiepileptic treatment itself. CLOCCs are rare radiological findings and must be recognized to avoid misdiagnosis. 2020-12-07T00:00:00Z