Key result
Deep learning using empirical mode decomposition accurately detects epileptic seizures from EEG signals.
Why the study?
Interpreting EEG data takes time and specialized expertise, requiring automated approaches for identifying epileptic seizures to enhance diagnosis and treatment.
Does a Deep Neural Network using empirical mode decomposition improve the accuracy of epileptic seizure classification from EEG data compared to existing methods?
Does a Deep Neural Network using empirical mode decomposition improve the accuracy of epileptic seizure classification from EEG data compared to existing methods?
A novel deep learning approach utilizing empirical mode decomposition of EEG signals provides high accuracy for automated epileptic seizure classification.
May aid automated EEG analysis in research; leaves open prospective clinical validation before practice adoption.
Uncontrollable, spontaneous seizures are a hallmark of the neurological condition epilepsy. Epilepsy diagnosis and monitoring frequently include electroencephalogram (EEG) data. It takes time and the skill of a qualified medical professional to interpret EEG data. In order to enhance the diagnosis and treatment of epilepsy, automated approaches for identifying epileptic episodes are required. The empirical mode decomposition (EMD) technique is used in this research to present a deep learning-based strategy for detecting epileptic seizures. The intrinsic mode functions (IMFs) that record various frequency components of the signal are used by the EMD algorithm to decompose EEG signals. We use a Deep Neural Network (DNN) to ascertain whether the input signal has seizure activity or not. The proposed technique improves existing methods, according to our experimental findings, which demonstrate high accuracy.
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Durga et al. (2023) studied Epilepsy. Deep learning-based strategy using empirical mode decomposition (EMD) and Deep Neural Network (DNN) vs. Existing methods was evaluated on Seizure detection accuracy. A deep learning-based strategy using empirical mode decomposition and a Deep Neural Network demonstrated high accuracy in detecting epileptic seizures from EEG signals.
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