Browsing by Author "Prendes, Jorge"

Now showing 1 - 2 of 2
  • Thumbnail Image
    artículo de publicación periódica.listelement.badge
    Study on epileptic seizure detection in EEG signals using largest Lyapunov exponents and logistic regression
    (2019) Quintero-Rincón, Antonio; Flugelman, Máximo; Prendes, Jorge; D'Giano, Carlos
    "Seizure 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 specificity."
  • Thumbnail Image
    ponencia en congreso.listelement.badge
    Study on spike-and-wave detection in epileptic signals using T-location-scale distribution and the K-nearest neighbors classifier
    (2017-12) Quintero-Rincón, Antonio; Prendes, Jorge; D'Giano, Carlos; Muro, Valeria
    "Pattern classification in electroencephalography (EEG) signals is an important problem in biomedical engineering since it enables the detection of brain activity, in particular the early detection of epileptic seizures. In this paper we propose a k-nearest neighbors classification for epileptic EEG signals based on an t-location-scale statistical representation to detect spike-and-waves. The proposed approach is demonstrated on a real dataset containing both spike-and-wave events and normal brain function signals, where our performance is evaluated in terms of classification accuracy, sensitivity and specificity."