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Automated diagnosis of EEG abnormalities with different classification techniques

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Abstract

Automatic seizure detection and prediction using clinical Electroencephalograms (EEGs) are challenging tasks due to factors such as low Signal-to-Noise Ratios (SNRs), high variance in epileptic seizures among patients, and limited clinical data constraints. To overcome these challenges, this paper presents two approaches for EEG signal classification. One of these approaches depends on Machine Learning (ML) tools. The used features are different types of entropy, higher-order statistics, and sub-band energies in the Hilbert Marginal Spectrum (HMS) domain. The classification is performed using Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbor (KNN) classifiers. Both seizure detection and prediction scenarios are considered. The second approach depends on spectrograms of EEG signal segments and a Convolutional Neural Network (CNN)-based residual learning model. We use 10000 spectrogram images for each class. In this approach, it is possible to perform both seizure detection and prediction in addition to a 3-state classification scenario. Both approaches are evaluated on the Children’s Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) dataset, which contains 24 EEG recordings for 6 males and 18 females. The results obtained for the HMS-based model showed an accuracy of 100%. The CNN-based model achieved accuracies of 97.66%, 95.59%, and 94.51% for Seizure (S) versus Pre-Seizure (PS), Non-Seizure (NS) versus S, and NS versus S versus PS classes, respectively. These results demonstrate that the proposed approaches can be effectively used for seizure detection and prediction. They outperform the state-of-the-art techniques for automatic seizure detection and prediction.

Graphical Abstract

Block diagram of proposed epileptic seizure detection method using HMS with different classifiers.

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Authors and Affiliations

  1. Department of Electronics and Communications, Delta Higher Institute for Engineering and Technology (DHIET), 35511, Mansoura, Egypt

    Essam Abdellatef

  2. Faculty of Electronic Engineering, Menoufia University, 32952, Menouf, Egypt

    Heba M. Emara, Fatma E. Ibrahim, Mohamed Elwekeil, Walid El-Shafai, Taha E. Taha, Adel S. El-Fishawy, El-Sayed M. El-Rabaie, Ibrahim M. Eldokany & Fathi E. Abd El-Samie

  3. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Mohamed R. Shoaib

  4. Security Engineering Laboratory, Department of Computer Science College of Engineering, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Walid El-Shafai

  5. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia

    Fathi E. Abd El-Samie

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  1. Essam Abdellatef
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  2. Heba M. Emara
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Correspondence to Walid El-Shafai.

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Abdellatef, E., Emara, H.M., Shoaib, M.R. et al. Automated diagnosis of EEG abnormalities with different classification techniques. Med Biol Eng Comput 61, 3363–3385 (2023). https://doi.org/10.1007/s11517-023-02843-w

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