Abstract
Epileptic focus localization by analysing intracranial electroencephalogram (iEEG) plays a critical role in successful surgical therapy of resection of the epileptogenic lesion. However, manual analysis and classification of the iEEG signal by clinicians are arduous and time-consuming and excessively depend on the experience. Due to individual differences of patients, the iEEG signal from different patients usually shows very diverse features even if the features belong to the same class. Accordingly, automatic detection of epileptic focus is required to improve the accuracy and to shorten the time for treatment. In this paper, we propose a novel feature fusion-based iEEG classification method, a deep learning model termed Time-Frequency Hybrid Network (TF-HybridNet), in which short-time Fourier transform (STFT) and 1d convolution layers are performed on the input iEEG in parallel to extract features of the time-frequency domain and feature maps. And then, the time-frequency features and feature maps are fused and fed to a 2d convolutional neural network (CNN). We used the Bern-Barcelona iEEG dataset for evaluating the performance of TF-HybridNet, and the experimental results show that our approach is able to differentiate the focal from nonfocal iEEG signal with an average classification accuracy of 94.3% and demonstrates an improved accuracy rate compared to the model using only STFT or one-dimensional convolutional layers as feature extraction.
Highlights
Epilepsy is a chronic disease of the brain, and it is characterized by recurrent and unpredictable seizures, which are brief episodes of involuntary movements and even accompanied by transient loss of consciousness [1]
It is crucial to determine the seizure area in surgical therapy, and there is a very strong demand for the automatic detection of epileptic focus localization. intracranial electroencephalogram (iEEG) is recorded directly from the cerebral cortex, and iEEG signals recorded from the epileptogenic area are more stationary and less random than iEEG
IEEG signal from different patients usually shows very diverse features due to individual differences of patients, even if the features belong to the same class
Summary
Epilepsy is a chronic disease of the brain, and it is characterized by recurrent and unpredictable seizures, which are brief episodes of involuntary movements and even accompanied by transient loss of consciousness [1]. Considering it is uncertain that symptoms will present in the iEEG signal at all times, and iEEG should be monitored and recorded in the long term During this process, huge amounts of data are generated and experienced neurological experts subsequently analyse abnormalities in brain activities via visual inspection. Huge amounts of data are generated and experienced neurological experts subsequently analyse abnormalities in brain activities via visual inspection This task is time-consuming that could lead to a serious delay of days or even weeks of treatment. IEEG is recorded directly from the cerebral cortex, and iEEG signals recorded from the epileptogenic area are more stationary and less random than iEEG signals recorded from the normal area [5] This nature makes it enable to be used for identification of location effectively
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