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- W2549229878 abstract "This paper presents a case study of non-Shannon entropy, i.e. Learning Entropy (LE), for instant detection of onset of epileptic seizures in individual EEG time series. Contrary to entropy methods of EEG evaluation that are based on probabilistic computations, we present the LE-based approach that evaluates the conformity of individual samples of data to the contemporary learned governing law of a learning system and thus LE can detect changes of dynamics on individual samples of data. For comparison, the principle and the results are compared to the Sample Entropy approach. The promising results indicate the LE potentials for feature extraction enhancement for early detection of epileptic seizures on individual-data-sample basis." @default.
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- W2549229878 date "2016-07-01" @default.
- W2549229878 modified "2023-10-18" @default.
- W2549229878 title "Study of Learning Entropy for onset detection of epileptic seizures in EEG time series" @default.
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- W2549229878 doi "https://doi.org/10.1109/ijcnn.2016.7727621" @default.
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