A novel 3D convolutional neural network based on multi-channel EEG achieved an overall accuracy of 92.37% for automatic seizure detection, outperforming 2D CNN and traditional machine learning methods.
Does a 3D CNN based on multi-channel EEG improve automatic seizure detection compared to 2D CNN and traditional methods in patients with complex partial seizures?
A 3D CNN utilizing multi-channel EEG data provides highly accurate, automated seizure detection, outperforming traditional 2D CNN and feature-based methods.
Absolute Event Rate: 92.37% vs 89.91%
BACKGROUND: Automated seizure detection from clinical EEG data can reduce the diagnosis time and facilitate targeting treatment for epileptic patients. However, current detection approaches mainly rely on limited features manually designed by domain experts, which are inflexible for the detection of a variety of patterns in a large amount of patients' EEG data. Moreover, conventional machine learning algorithms for seizure detection cannot accommodate multi-channel Electroencephalogram (EEG) data effectively, which contains both temporal and spatial information. Recently, deep learning technology has been widely applied to perform image processing tasks, which could learns useful features from data and process multi-channel data automatically. To provide an effective system for automatic seizure detection, we proposed a new three-dimensional (3D) convolutional neural network (CNN) structure, whose inputs are multi-channel EEG signals. METHODS: EEG data of 13 patients were collected from one center hospital, which has already been inspected by experts. To represent EEG data in CNN, firstly time series of each channel of EEG data was converted into the two-dimensional image. Then all channel images were combined into 3D images according to the mutual correlation intensity between different electrodes. Finally, a CNN was constructed using 3D kernels to predict different stages of EEG data, including inter-ictal, pre-ictal, and ictal stages. The system performance was evaluated and compared with the traditional feature-based classifier and the two-dimensional (2D) deep learning method. RESULTS: It demonstrated that multi-channel EEG data could provide more information for increasing the specificity and sensitivity in cpmparison result between the single and multi-channel. And the 3D CNN based on multi-channel outperformed the 2D CNN and traditional signal processing methods with an accuracy of more than 90%, an sensitivity of 88.90% and an specificity of 93.78%. CONCLUSIONS: This is the first effort to apply 3D CNN in detecting seizures from EEG. It provides a new way of learning patterns simultaneously from multi-channel EEG signals, and demonstrates that deep neural networks in combination with 3D kernels can establish an effective system for seizure detection.
Wei et al. (Sat,) conducted a other in Epilepsy (complex partial seizures) (n=13). Three-dimensional Convolutional Neural Network (3D CNN) based on multi-channel EEG vs. 2D CNN and traditional machine learning (ApEn+DWT + SVM) was evaluated on Mean accuracy of epileptic stage classification. A novel 3D convolutional neural network based on multi-channel EEG achieved an overall accuracy of 92.37% for automatic seizure detection, outperforming 2D CNN and traditional machine learning methods.