EPILEPTIC SEIZURE DETECTION FROM EEG SIGNALS WITH RECURRENT NEURAL NETWORKS BASED CLASSIFICATION MODEL

Year 2024, Volume: 7 Issue: 2, 14 - 21, 18.12.2024

Serpil Aslan , Harun Bingöl

https://doi.org/10.54565/jphcfum.1500546

Abstract

Epileptic seizures are a neurological disorder that occurs as a result of sudden and uncontrolled electrical activities of the non-contagious brain. This condition may cause the person to lose normal activities temporarily. Epileptic seizures are a severe disease that affects approximately 60 million people in the world, usually manifested by symptoms such as loss of consciousness, muscle twitching, sudden sensory changes, or behavioural changes [1]. Genetics, brain injury, hormonal fluctuations, infections, or metabolic problems are some of the possible causes of epileptic seizures. Although the severity and duration of the seizure varies from person to person, it is usually very short and rarely reaches a point where it endangers human life. However, such seizures need to be recognized as soon as possible in order to improve the quality of life of individuals and reduce the frequency of seizures. Epileptic seizures are a manageable disease with early diagnosis and appropriate treatment. Recognizing epileptic seizures begins with understanding a person's symptoms and triggering factors. These symptoms may include loss of consciousness, muscle twitches, sudden sensory changes, and behavioural changes. The symptoms of seizures, past medical history, and neurological examinations are essential in the diagnosis process. From past to present, many methods have been developed for the early diagnosis and detection of epileptic seizures [2]. One of these is analyzing the brain's neural activities using electroencephalography (EEG), which helps experts make a diagnosis. Although EEG signals are used as a powerful tool in epileptic seizure recognition, distinguishing the signals within them is both costly and requires highly expert experience. Therefore, this study proposed an automatic classification model for pre-processed EEG signals using Dual-Tree Complex Wavelet Transform (DT-CWT) based on deep learning-based Recurrent Neural Networks (RNN) architecture to assist experts. Compared to classical machine learning methods, deep learning-based models require less manual feature engineering because they perform data automatically thanks to deep networks instead of manually selecting and transforming the data features. These advantages make the model more general and flexible. The proposed model aims to classify EEG signals and detect epileptic seizures effectively and quickly in the early stages.

Keywords

Deep Learning, Epilepstic Seizure, RNN, BiLSTM, CNN

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