Classification of Seizure Types Using Random Forest Classifier

• Autorzy: Basri Ashjan , Arif Muhammad

Identyfikatory

DOI

10.12913/22998624/140542

• Języki publikacji: EN

Abstrakty

EN

Epilepsy is one of the most common mental disorders in the world, affecting 65 million people. The prevalence in Arab countries of Epilepsy is estimated at 174 per 100,000 individuals, and in Saudi Arabia is 6.54 per 1,000 individuals. Epilepsy seizures have different types, and each patient needs to have a treatment plan according to the seizure type. Hence, accurate classification of seizure type is an essential part of diagnosing and treating epileptic patients. In this paper, features based on fast Fourier transform from EEG montages are used to classify different types of seizures. Since the distribution of classes is not uniform and the dataset suffers from severe imbalance. Various algorithms are used to under-sample the majority class and over-sample the minority classes. Random forest classifier produced classification accuracy of 96% to differentiate three types of seizures from the healthy EEG reading.

Słowa kluczowe

EN

EEG; fast fourier transform; seizure; random forest

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2021
• Tom: Vol. 15, no 3
• Strony: 167--178
• Opis fizyczny: Bibliogr. 70 poz., fig., tab.

Twórcy

autor

Basri Ashjan

• Department of Computer Science Umm Alqura University, Makkah, Saudi Arabia

autor

Arif Muhammad

• Department of Computer Science Umm Alqura University, Makkah, Saudi Arabia

• https://orcid.org/0000-0003-0513-9872

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).