seizures, which severely impact the quality of life of epilepsy patients and sometimes are accompanied by loss of consciousness. The most widely accepted and used tool by epileptologists to identify seizures and diagnose epilepsy is the ElectroEncephaloGram (EEG). Seizure detection on EEG signals is a long process, which is done manually by epileptologists. This paper describes how to analyze EEG signal using Data Mining methods and techniques with the main objective of automatically detect a seizure within EEG signals. We have designed and developed a multipurpose and extendable tool for feature extraction from time series data, named Training Builder. Our trained classifier, based on signal processing, sliding window paradigm, features extraction and selection, and Support Vector Machines, showed excellent results, reaching an accuracy of over 99% during the test made on publicly available EEG datasets.

EEG signal analysis for epileptic seizures detection by applying Data Mining techniques / Zazzaro, Gaetano; Cuomo, Salvatore; Martone, Angelo; Montaquila, ROBERTO VALENTINO; Toraldo, Gerardo; Pavone, Luigi. - In: INTERNET OF THINGS. - ISSN 2542-6605. - 14:(2021), p. 100048. [10.1016/j.iot.2019.03.002]

EEG signal analysis for epileptic seizures detection by applying Data Mining techniques

Cuomo, Salvatore;MONTAQUILA, ROBERTO VALENTINO;Toraldo, Gerardo;
2021

Abstract

seizures, which severely impact the quality of life of epilepsy patients and sometimes are accompanied by loss of consciousness. The most widely accepted and used tool by epileptologists to identify seizures and diagnose epilepsy is the ElectroEncephaloGram (EEG). Seizure detection on EEG signals is a long process, which is done manually by epileptologists. This paper describes how to analyze EEG signal using Data Mining methods and techniques with the main objective of automatically detect a seizure within EEG signals. We have designed and developed a multipurpose and extendable tool for feature extraction from time series data, named Training Builder. Our trained classifier, based on signal processing, sliding window paradigm, features extraction and selection, and Support Vector Machines, showed excellent results, reaching an accuracy of over 99% during the test made on publicly available EEG datasets.
2021
EEG signal analysis for epileptic seizures detection by applying Data Mining techniques / Zazzaro, Gaetano; Cuomo, Salvatore; Martone, Angelo; Montaquila, ROBERTO VALENTINO; Toraldo, Gerardo; Pavone, Luigi. - In: INTERNET OF THINGS. - ISSN 2542-6605. - 14:(2021), p. 100048. [10.1016/j.iot.2019.03.002]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/767267
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