Abstract
Automated seizure detection system based on electroencephalograms (EEG) is an interdisciplinary research problem between computer science and neuroscience. Epileptic seizure affects 1% of the worldwide population and can lead to severe long-term harm to safety and life quality. The automation of seizure detection can greatly improve the treatment of patients. In this work, we propose a neural network model to extract features from EEG signals with a method of arranging the dimension of feature extraction inspired by the traditional method of neurologists. A postprocessor is used to improve the output of the classifier. The result of our seizure detection system on the TUSZ dataset reaches a false alarm rate of 12 per 24 hours with a sensitivity of 59%, which approaches the performance of average human detector based on qEEG tools.
Highlights
Electroencephalograph (EEG) recording refers to the measurement of electrical activity resulting from postsynaptic potentials within the brain [1]
We introduced a system for automatic real-time detection of epileptic EEG events
Multiple feature extractors are used in our proposed Discrete wavelet transformation (DWT)-Net to guide the feature extraction behavior of the model and improve its ability to incept local temporal and spatial features
Summary
Electroencephalograph (EEG) recording refers to the measurement of electrical activity resulting from postsynaptic potentials within the brain [1]. Considerable amounts of works have been done with this two-step procedure for better detection accuracy, including time-frequency feature map with a support vector machine (SVM) [3, 7], nonlinear features with different types of classifiers [5, 8, 9], and features based on time-frequency image with image recognition methods [10, 11] These researches have provided different methodologies for seizure detection. The authors tried several topologies over the number of channels to be convoluted together, the accuracy is limited due to insufficient representative features in EEG recordings Both works introduced the idea of manually adjusting the input domain in the early stage of the neural network in seek of better performance of their model.
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