Version 1
: Received: 8 September 2023 / Approved: 11 September 2023 / Online: 12 September 2023 (10:12:33 CEST)
How to cite:
Al-Qazzaz, N.; Alrahhal, M.; Jaafer, S.; Ali, S.; Ahmad, S. Automatic Diagnosis of Epileptic Seizures using Entropy-based Features and Multimodel Deep Learning Approaches. Preprints2023, 2023090714. https://doi.org/10.20944/preprints202309.0714.v1
Al-Qazzaz, N.; Alrahhal, M.; Jaafer, S.; Ali, S.; Ahmad, S. Automatic Diagnosis of Epileptic Seizures using Entropy-based Features and Multimodel Deep Learning Approaches. Preprints 2023, 2023090714. https://doi.org/10.20944/preprints202309.0714.v1
Al-Qazzaz, N.; Alrahhal, M.; Jaafer, S.; Ali, S.; Ahmad, S. Automatic Diagnosis of Epileptic Seizures using Entropy-based Features and Multimodel Deep Learning Approaches. Preprints2023, 2023090714. https://doi.org/10.20944/preprints202309.0714.v1
APA Style
Al-Qazzaz, N., Alrahhal, M., Jaafer, S., Ali, S., & Ahmad, S. (2023). Automatic Diagnosis of Epileptic Seizures using Entropy-based Features and Multimodel Deep Learning Approaches. Preprints. https://doi.org/10.20944/preprints202309.0714.v1
Chicago/Turabian Style
Al-Qazzaz, N., Sawal Ali and Siti Ahmad. 2023 "Automatic Diagnosis of Epileptic Seizures using Entropy-based Features and Multimodel Deep Learning Approaches" Preprints. https://doi.org/10.20944/preprints202309.0714.v1
Abstract
One of the most prevalent brain diseases, epilepsy is characterized by recurring seizures that happen quite frequently. During seizures, a patient suffers uncontrollable muscle contractions that cause loss of motion and balance, which could lead to harm or even death. Establishing an automatic method for warning patients about impending seizures requires extensive research. It is possible to anticipate seizures by analyzing the Electroencephalogram (EEG) signal from the scalp region of the human brain. Time domain-based features such as Hurst exponent (Hur), Tsallis Entropy (TsEn), improved permutation entropy (impe), and amplitude-aware permutation entropy (AAPE) were extracted from EEG signals. In order to diagnose epileptic seizure children from normal children automatically, this study conducted two sessions, in the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), while in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers including a gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional (BiLSTM). The EEG dataset obtained from the Neurology Clinic at the Ibn-Rushd Training Hospital. In this regard, detailed explanations and research from the time domain and entropy characteristics show that using GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All−time−entropy fusion feature improves the final classification results.
Keywords
EEG; epileptic seizure; entropy; deep learning; LSTM; BiLSTM; GRU; classification
Subject
Engineering, Bioengineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
