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

Abstract

BackgroundAutomated 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.MethodsEEG 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.ResultsIt 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%.ConclusionsThis 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.

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

“…In this way, each time window, or segment, has 1280 data points in the 11D space. e data segmentation allows to increase the number of training samples, which is mandatory, especially in deep learning [26,29]. For the training of the network, the data from seizures were segmented with an overlap with the previous window of 0.1484 s, 0.5508 s, 1 s, for patient #5, patient #6, and patient #9, respectively.…”

“…e results, presented in terms of median true positive rate labelling by seconds is 74%, which the authors claimed to be higher than that of commercially available seizure detection software. In [29], each single-channel EEG signal is converted into a 2D plot and the plots corresponding to 22 different EEG channels are combined in 3D images depending on the mutual correlation of the intensities between the electrodes. en, using 3D kernels, a 3D CNN, with 4 convolutional layers, 3 max pooling layers, and 2 fully connected layers, has been built to detect interictal, preictal, and ictal stages in 13 epileptic patients.…”

Automatic seizure detection based on imaged-EEG signals through fully convolutional networks

“…For each input modality, different architectures were evaluated depending on the dimensionality of the data (1D or 2D). In another study, the authors proposed the use of 3D inputs from multi-channel EEG recordings 22 ; the results over 13 patients with 159 seizures demonstrated that 3D models surpassed 2D models in metrics such as accuracy, specificity and sensitivity in about two points. However, 3D models have a larger number of parameters to be learned, and thus require much more training samples.…”

Comparison of different input modalities and network structures for deep learning-based seizure detection

“…These studies have been based on different deep neural network structures, such as a fully connected neural network (FCNN) 24 , convolutional neural network (CNN) 22,[25][26][27] , and recurrent neural network (RNN) 28 . These different neural networks can automatically learn discriminative features from various types of data input, including raw temporal EEG 26 , FFT results 25 , 2-dimensional (2D) representation of STFT results 29 , and 2D images of raw EEG 27 . The adoption of different input forms and network structures typically makes it difficult to directly compare performance among different deep learning methods.…”

“…Furthermore, these studies adopted different window sizes for EEG segmentation (e.g., 1-25 , 2-29 , 3-22 , 5- 24,27 , 8-26 , and 23.6-s 28 windows). In addition, the classifiers were trained and tested on different datasets including public EEG datasets such as the Bonn 22,28 , CHB-MIT 25,29 , and Freiburg 25 datasets, or their own datasets 24,27 . Thus, it is almost impossible to directly compare the results of different studies to ascertain an optimal combination of input modalities and network structures.…”