Epileptic Seizure Recognition Using Improved Modes Decomposition and Online Sequential Autoencoder Multi-Kernel Broad Learning System

In this article, improved variational mode decomposition (IVMD) and the online sequential autoencoder multi-kernel broad learning system (OSAEMKBLS) are integrated to recognize epileptic seizure (ES) epochs from both multichannel and single-channel electroencephalogram (EEG) recordings. The proposed IVMD extracts the optimum number of efficient band-limited intrinsic mode functions (BLIMFs) and the data fidelity factor ( $\alpha $ ) using the irregularity index-based Tsallis entropy as a cost function. The designed autoencoder in the proposed OSAEMKBLS architecture is utilized to extract the most elucidative unsupervised signatures from selected informative BLIMFs, chunk by chunk sequentially. These signatures are then fed into the novel supervised kernel trick-based broad learning system for the efficacious recognition of seizure epochs, based on the root mean square error (RMSE) optimal cost function. The efficacy of the proposed IVMD-OSAEMKBLS algorithm is evaluated using benchmark multichannel scalp EEG (sEEG) and single-channel EEG datasets. The proposed method demonstrates higher learning speed, lower computational complexity, better model generalization, and a lower false positive rate per hour (FPR/h) at 0.019. It achieves outstanding recognition accuracy at 99.98% and a short-event recognition time of 42 ms, compared to the IVMD-BLS, IVMD-OSBLS, and IVMD-OSMKBLS methods. Finally, reconfigurable field-programmable gate array (FPGA) hardware is employed to implement the novel IVMD-OSAEMKBLS, developing a computer-aided diagnosis (CAD) system for the automated diagnosis of ES patients. The integrity and expediency of the proposed algorithm endorse secure and admirable accomplishments in seizure detection and recognition.