Robust Classifier Selection for Cost-Effective EEG Seizure Detection in Remote Areas

Seizure detection using electroencephalogram (EEG) signals plays a crucial role in diagnosing and treating epilepsy. However, the cost associated with deploying EEG-based seizure detection systems limits their widespread adoption, particularly in resource-constrained healthcare settings like remote areas in countries like Bangladesh. This research aims at fostering cost-efficient, fully automated initial seizure diagnosis in remote areas. This paper presents a novel approach for classifier selection in EEG seizure detection systems to improve cost-effectiveness while maintaining high detection accuracy. Two publicly available datasets of EEG signals from a low-cost consumer-grade EEG headset with 14 channels have been chosen to ensure lower cost during data acquisition along with lower complexity of classifier design. Fourteen state-of-the-art classifiers found in recent literature have been used to distinguish between epileptic individuals and healthy individuals. Firstly, we have chosen the four most accurate classifiers which are ChronoNet (94.6%), CNN-LSTM (92.5%), IC-RNN (91.8%), and C-DRNN (88.6%). Then further compared these four models in terms of evaluation time (ET), and trainable parameters (TP) along with their Cohen's kappa score and discarded the more complex CNN-LSTM model due to its extremely high ET (1.48s) and TP (about 2.2 million). We have also investigated the sensitivity of individual channels on the best-performing classifiers and found that most sensitive channels lie in the frontal and parietal zone. After comparing the robustness due to failure of any EEG signal channel, the C-DRNN has been found the most robust and the fastest for the initial diagnosis with the least resources.