Automatic seizure detection using three-dimensional CNN based on multi-channel EEG

• Published in: BMC medical informatics and decision making Vol. 18; no. Suppl 5; p. 111
• Main Authors: Wei, Xiaoyan, Zhou, Lin, Chen, Ziyi, Zhang, Liangjun, Zhou, Yi
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 07.12.2018; BioMed Central; BMC
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Automation; Brain research; Convolutional neural network; Convulsions & seizures; Crowdsourcing; Data processing; Deep learning; Diagnosis; EEG; Electrodes; Electroencephalography; Electroencephalography - methods; Epilepsy; Epilepsy - diagnosis; Humans; Image processing; Kernels; Learning algorithms; Machine Learning; Medical imaging; Methods; Multi-channel; Neural networks; Neural Networks, Computer; Patients; Process controls; Seizing; Seizure detection; Seizures; Seizures (Medicine); Seizures - diagnosis; Sensitivity; Signal processing; Signal Processing, Computer-Assisted; Spatial data; Three dimensional imaging; Three-dimensional; Time series; China; Multi-channel; Seizure detection; Three-dimensional; Convolutional neural network; Epilepsy
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-018-0693-8

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. 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. 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%. 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.