2022
DOI: 10.21203/rs.3.rs-2100432/v1
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Seven Epileptic Seizure Type Classification in Pre-Ictal, Ictal and Inter-Ictal Stages Using Machine Learning Techniques

Abstract: Background Based on the symptoms experienced during the episode and the Electroencephalograph (EEG) recording made during the inter-ictal phase, the doctor makes the epileptic seizure type diagnosis. The fundamental issue, however, is that patients frequently struggle to explain their symptoms in the absence of an observer and identify traces in inter-ictal EEG patterns. Aims This study examines electroencephalographic (EEG) signals from epileptic seizures in order to diagnose seizures in pre-ictal, ictal, a… Show more

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Cited by 2 publications
(2 citation statements)
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“…The K-nearest neighbor (KNN) algorithm has validated the solution by distinguishing an epilepsy group (EG) against a healthy control (HC). Afterward, a psychogenic kind of disease was tested using another group of patients characterized by non-epileptic disorder (NEAD) [8], [9].…”
Section: Introductionmentioning
confidence: 99%
“…The K-nearest neighbor (KNN) algorithm has validated the solution by distinguishing an epilepsy group (EG) against a healthy control (HC). Afterward, a psychogenic kind of disease was tested using another group of patients characterized by non-epileptic disorder (NEAD) [8], [9].…”
Section: Introductionmentioning
confidence: 99%
“…A total of nine connectivity features based on five different measures in time, frequency, and timefrequency domains have been tested. The solution has been validated by the K-Nearest Neighbour algorithm, classifying an epilepsy group (EG) vs. a healthy control (HC), and subsequently, with another cohort of patients characterised by non-epileptic attacks (NEAD), a psychogenic type of disorder was tried out [8,9].…”
Section: Introductionmentioning
confidence: 99%