Key result
A hybrid 1D CNN-machine learning approach achieved classification accuracies of 94% to 100% on the University of Bonn dataset and 98% on the CHB-MIT dataset for epileptic seizure detection.
Why the study?
Visual inspection of complex EEG signals is costly and time-consuming, while existing machine learning and deep learning approaches require manual feature extraction, are computationally intensive, or have weak generalization ability.
Does a hybrid 1D CNN-Machine Learning approach improve epileptic seizure detection accuracy from EEG data?
Does a hybrid 1D CNN-Machine Learning approach improve epileptic seizure detection accuracy from EEG data?
A hybrid 1D CNN and machine learning approach achieves high accuracy in detecting epileptic seizures from EEG data across multiple datasets.
High accuracies on benchmark EEG datasets encourage model refinement; leaves open prospective clinical validation before practice adoption.
Electroencephalography (EEG) is a widely used technique for the detection of epileptic seizures. It can be recorded in a noninvasive manner to present the electrical activity of the brain. The visual inspection of nonlinear and highly complex EEG signals is both costly and time-consuming. Therefore, an effective automatic detection system is needed to assist in the long-term evaluation and treatment of patients. Traditional approaches based on machine learning require feature extraction, while deep learning approaches are time-consuming and require more layers for effective feature learning and processing of complex EEG waveforms. Deep learning-based approaches also have weak generalization ability. This paper proposes a solution based on the combination of convolution neural networks (CNN) and machine learning classifiers. It preprocesses the EEG signal using the Butterworth filter and performs feature extraction using CNN. From the extracted set of features, the approach selects only the relevant features using mutual information-based estimators to reduce the curse of dimensionality and improve classification accuracy. The selected features are then passed as input to different machine learning classifiers. The suggested solution is evaluated on the University of Bonn dataset and CHB-MIT datasets. Our model effectively predicts 2, 3, 4, and 5 classes with accuracy of 100%, 99%, 94.6%, and 94%, respectively, for the Bonn dataset and 98% for CHB-MIT datasets.
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Hassan et al. (2022) studied Epileptic seizures. Hybrid 1D CNN-Machine Learning Approach was evaluated on Classification accuracy for seizure detection. A hybrid 1D CNN-machine learning approach achieved classification accuracies of 94% to 100% on the University of Bonn dataset and 98% on the CHB-MIT dataset for epileptic seizure detection.
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