Key result
A convolutional neural network (CNN) using EEG signals improved the accuracy and robustness of automatic epileptic seizure detection compared to traditional models.
Why the study?
Manual detection of epileptic seizures from EEG signals requires highly skilled specialists and is prone to error, while achieving robust and accurate automatic detection remains challenging.
Does a convolutional neural network improve the accuracy and robustness of epileptic seizure detection using EEG signals compared to traditional models?
Does a convolutional neural network improve the accuracy and robustness of epileptic seizure detection using EEG signals compared to traditional models?
A CNN-based approach using EEG signals provides an automated method for detecting epileptic seizures, potentially improving accuracy over traditional models.
May improve automated EEG seizure detection; leaves open prospective clinical validation before adoption.
Epileptic seizures are observed in almost all age groups and has become a predominant issue. Electroencephalogram (EEG) is a very common modality used to record the brain signals. Understanding the cortical connectivity of brain is still remaining as a challenging task and needs highly skilled neuro specialists. Humans are prone to error and so as doctors too, hence the automatic detection of such seizures is very important in order to take proper action. Although many approaches have been tried by many researchers to achieve a better accuracy and the robustness is still a challenging task. In this work, convolutional neural network (CNN) is used to achieve a better accuracy with robustness using EEG signals. Very few convolutional layers are used along with Relu as activation function. CNN is fed with these EEG signals for classification purpose. This model works well in contrast to the present traditional models.
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Mahajan et al. (2020) studied Epileptic seizures. Convolutional neural network (CNN) vs. Traditional models was evaluated on Accuracy and robustness of seizure detection. A convolutional neural network (CNN) using EEG signals improved the accuracy and robustness of automatic epileptic seizure detection compared to traditional models.
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