Abstract
The key research aspects of detecting and predicting epileptic seizures using electroencephalography (EEG) signals are feature extraction and classification. This paper aims to develop a highly effective and accurate algorithm for seizure prediction. Efficient channel selection could be one of the solutions as it can decrease the computational loading significantly. In this research, we present a patient-specific optimization method for EEG channel selection based on permutation entropy (PE) values, employing K nearest neighbors (KNNs) combined with a genetic algorithm (GA) for epileptic seizure prediction. The classifier is the well-known support vector machine (SVM), and the CHB-MIT Scalp EEG Database is used in this research. The classification results from 22 patients using the channels selected to the patient show a high prediction rate (average 92.42%) compared to the SVM testing results with all channels (71.13%). On average, the accuracy, sensitivity, and specificity with selected channels are improved by 10.58%, 23.57%, and 5.56%, respectively. In addition, four patient cases validate over 90% accuracy, sensitivity, and specificity rates with just a few selected channels. The corresponding standard deviations are also smaller than those used by all channels, demonstrating that tailored channels are a robust way to optimize the seizure prediction.
Highlights
Epilepsy is a serious brain disorder, second only to strokes in its effect
The process divided into by K nearest neighbors (KNNs) combined with a genetic algorithm (GA) and (KNN-GA)
The results demonstrate that the proposed EEG channel selection method with a suitable classification algorithm (SVM in this paper) can increase real-time seizure prediction accuracy
Summary
Epilepsy is a serious brain disorder, second only to strokes in its effect. More than 50 million people worldwide are affected by epilepsy, and the symptoms of one-third of those are not controlled by anticonvulsant medication. One of the critical objectives in seizure management in epileptic patients is its early detection and prediction to provide well-timed preventive interventions [1]. If epileptic seizures can be predicted in advance, the patients’ unfortunate consequences can be alleviated. Despite decades of international efforts devoted to predicting seizures, seizure prediction remains an unsolved problem [2]. Two key components in research into seizure detection and prediction using epileptic electroencephalography (EEG) signals are feature extraction and classification [3,4]
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