Why the study?
Does a deep learning technique based on multilayer perceptrons accurately detect epileptic seizures from EEG signals?
Does a deep learning technique based on multilayer perceptrons accurately detect epileptic seizures from EEG signals?
A deep learning model using multilayer perceptrons and frequency domain features achieved approximately 95% F-measure accuracy in detecting epileptic seizures from EEG signals.
May support automated EEG seizure detection; leaves open prospective validation before clinical use.
Millions of people around the world suffer from epilepsy. It is very important to provide a method to efficiently monitor the seizures and alert the caregivers to help patients. It is proven that EEG signals are the best markers for diagnosis of the epileptic seizures. In this paper, we used the frequency domain features (normalized in-band power spectral density) to extract information from EEG signals. We applied a deep learning technique based on multilayer perceptrons to improve the accuracy of seizure detection. The results indicate that our nonlinear technique is able to efficiently and automatically detect seizure and non-seizure episodes with an F-measure accuracy of around 95%.
No takes yet. Share an insight, caveat, or question.
Birjandtalab et al. (2017) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: