Epilepsy seizure detection with a majority voting classifier using logistic regression

Paul Oswald; Nida Nasir; Feras Barneih; Omar Alshaltone; Mohammad AlShabi; Talal Bonny; Ahmed Al-shammaa

doi:10.1117/12.2664003

14 June 2023 Epilepsy seizure detection with a majority voting classifier using logistic regression

Paul Oswald, Nida Nasir, Feras Barneih, Omar Alshaltone, Mohammad AlShabi, Talal Bonny, Ahmed Al-shammaa

Proceedings Volume 12548, Smart Biomedical and Physiological Sensor Technology XX; 125480J (2023) https://doi.org/10.1117/12.2664003
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

Epilepsy is a neurological condition caused by sudden onsets of electrical activity in the brain. This results in frequent, uncommon seizures, which can lead to severe physical consequences. In a clinical setting, data recorded using EEG (Electroencephalogram) is used to help diagnose the condition. This research focuses on the use of Short-Term Fourier transform (STFT) and feature extraction in the EEG data for the use in a majority voting model using logistic regression (LR) to detect the presence of epileptic seizures in the five EEG frequency bands ( i.e. Alpha, Beta, Gamma, Delta, and Theta). To quantify, a number of evaluation metrics have been calculated. Overall, the model was able to achieve an accuracy of up to 92%.

Citation Download Citation

Paul Oswald, Nida Nasir, Feras Barneih, Omar Alshaltone, Mohammad AlShabi, Talal Bonny, and Ahmed Al-shammaa "Epilepsy seizure detection with a majority voting classifier using logistic regression", Proc. SPIE 12548, Smart Biomedical and Physiological Sensor Technology XX, 125480J (14 June 2023); https://doi.org/10.1117/12.2664003

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