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• W2572930673 abstract "An innovative method based on topological data analysis is introduced for classifying EEG recordings of patients affected by epilepsy. We construct a topological space from a collection of EEGs signals using Persistent Homology; then, we analyse the space by Persistent entropy, a global topological feature, in order to classify healthy and epileptic signals.The performance of the resulting one-feature-based linear topological classifier is tested by analysing the Physionet dataset. The quality of classification is evaluated in terms of the Area Under Curve (AUC) of the receiver operating characteristic curve. It is shown that the linear topological classifier has an AUC equal to [Formula: see text] while the performance of a classifier based on Sample Entropy has an AUC equal to 62.0%." @default.
• W2572930673 created "2017-01-26" @default.
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• W2572930673 date "2018-06-14" @default.
• W2572930673 modified "2023-10-06" @default.
• W2572930673 title "Topological classifier for detecting the emergence of epileptic seizures" @default.
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• W2572930673 doi "https://doi.org/10.1186/s13104-018-3482-7" @default.
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