Key result
A convolutional neural network using eight features extracted via discrete wavelet transform achieved 96.5% accuracy in classifying EEG signals for epilepsy seizure detection.
Why the study?
To evaluate the effect of individual features combined with convolutional neural networks to identify epileptic seizures in EEG signals.
Does a combination of Discrete Wavelet Transform and Convolutional Neural Networks improve the classification accuracy of epilepsy seizures in EEG signals?
Does a combination of Discrete Wavelet Transform and Convolutional Neural Networks improve the classification accuracy of epilepsy seizures in EEG signals?
A deep learning model combining Discrete Wavelet Transform and Convolutional Neural Networks achieved high accuracy in detecting epilepsy seizures from EEG signals.
In this study, the capability to study the effect of each feature on the accuracy of the classification, whereby in the mixture of features with the Convolutional Neural Networks (CNNs) to identify epilepsy seizure in EEGs was searched. The EEG signals were first analyzed within 5 subsignals at specific frequencies bands by using Discrete Wavelet Transforms (DWT) at 5 levels, and then features were extracted from each sub signal. Finally, there was convolutional neural network classification. The best classification accuracies obtained when extracted eight features from EEG signals 96.5%. That means these features are strong to catch epilepsy seizure. Usually, the smart methods could be utilized within a more broad range of identification model problems that are also relevant to humans, such as the epilepsy diseases discovery and judgment.
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ABUKHETTALA et al. (2022) studied Epilepsy. Convolutional Neural Networks with Discrete Wavelet Transform vs. Previous machine learning methods was evaluated on Classification accuracy for detecting epilepsy seizures. A convolutional neural network using eight features extracted via discrete wavelet transform achieved 96.5% accuracy in classifying EEG signals for epilepsy seizure detection.
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