Key result
DWT-EMD feature level fusion improved seizure detection performance across multiple classifiers compared to individual DWT and EMD features on two benchmark EEG datasets.
DWT-EMD feature level fusion improves seizure detection performance across multiple machine learning classifiers compared to individual features.
DWT-EMD fusion for EEG seizure detection is hypothesis-generating; prospective validation required before clinical consideration.
Brain Computer Interface technology enables a pathway for analyzing EEG signals for seizure detection. EEG signal decomposition, features extraction and machine learning techniques are more familiar in seizure detection. However, selecting decomposition technique and concatenation of their features for seizure detection is still in the state-of-the-art phase. This work proposes DWT-EMD Feature level Fusion-based seizure detection approach over multi and single channel EEG signals and studied the usability of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) feature fusion with respect to individual DWT and EMD features over classifiers SVM, SVM with RBF kernel, decision tree and bagging classifier for seizure detection. All classifiers achieved an improved performance over DWT-EMD feature level fusion for two benchmark seizure detection EEG datasets. Detailed quantification results have been mentioned in the Results section.
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Jana et al. (2022) studied Seizure. DWT-EMD Feature level Fusion vs. Individual DWT and EMD features was evaluated on Seizure detection performance. DWT-EMD feature level fusion improved seizure detection performance across multiple classifiers compared to individual DWT and EMD features on two benchmark EEG datasets.
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