Key result
The proposed deep ensemble network with empirical wavelet transform achieved a classification accuracy of 98.93% for the UoB dataset and 98% for the NSC-ND dataset in detecting epileptic seizures.
Why the study?
Epileptic seizures can cause death, making detection and diagnosis important, and automated EEG signal processing using machine learning can facilitate detection compared to manual analysis.
Does a stacked ensemble deep neural network model with empirical wavelet transform improve the detection of epilepsy seizures from EEG signals?
Does a stacked ensemble deep neural network model with empirical wavelet transform improve the detection of epilepsy seizures from EEG signals?
Absolute Event Rate: 98.93% vs 95.01%
A stacked ensemble deep neural network using empirical wavelet transform features achieved high accuracy (up to 98.93%) in detecting epileptic seizures from EEG signals.
May aid automated EEG seizure detection; hypothesis-generating and requires prospective clinical validation before adoption.
Epileptic seizure attack is caused by abnormal brain activity of human subjects. Certain cases will lead to death. The detection and diagnosis is therefore an important task. It can be performed either by direct patient activity during seizure or by electroencephalogram (EEG) signal analysis by neurologists. EEG signal processing and detection of seizures using machine learning techniques make this task easier than manual detection. To overcome this problem related to a neurological disorder, we have proposed the ensemble learning technique for improved detection of epilepsy seizures from EEG signals. In the first stage, EEG signal decomposition is done by utilizing empirical wavelet transform (EWT) for smooth analysis in terms of sub-bands. Further, features are extracted from each sub. Time and frequency domain features are the two categories used to extract the statistical features. These features are used in a stacked ensemble of deep neural network (DNN) model along with multilayer Perceptron (MLP) for the detection and classification of ictal, inter-ictal, and pre-ictal (normal) signals. The proposed method is verified using two publicly available datasets provided by the University of Bonn (UoB dataset) and Neurology and Sleep Center - New Delhi (NSC-ND dataset). The proposed algorithm resulted in 98.93 % and 98 % accuracy for the UoB and NSC-ND datasets, respectively.
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Panda et al. (2021) studied Epilepsy (n=650). Deep Ensemble Network with Empirical Wavelet Transform vs. Direct EEG dataset training without EWT and statistical features was evaluated on Classification accuracy for ictal versus inter-ictal signals. The proposed deep ensemble network with empirical wavelet transform achieved a classification accuracy of 98.93% for the UoB dataset and 98% for the NSC-ND dataset in detecting epileptic seizures.
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