Automated detection of the preseizure state in EEG signal using neural networks - Sudalaimani, C.; Sivakumaran, N.; Elizabeth, Thomas T.; Rominus, Valsalam S. (Biocybernetics and biomedical engineering, 2019)
The life-threatening neural syndrome epilepsy is elicited by seizure which affects over 50 million people in the universal. A seizure is a brain condition made by excessive, unusual exoneration by nerve cells of the brain. Contemporary seizure forecast research works exhibited worthy results in both undersized and lengthy electroencephalography (EEG) signal; however it is essential to formulate superior epileptic seizure forecast system; that shall be steady, constant and less resource intensive for effectively employed to heading for evolving a convenient and easily manageable ictal or seizure forewarning prearrangement or devices. Based on our exploration, we have found a novel seizure prediction method which we evaluated by producing ten sub-frequency EEG data from initially recorded signal. Simple, robust and computationally less-intense EEG characteristics are mined using the generated sub-frequency signals and applied the extracted features to computationally less intense generalized regression neural network (GRNN) to segregate EEG signal clips into normal or preseizure files. In this research work, we have engendered 10 sub-frequency bands of signals from original EEG recordings, extracted various meaningful features from those sub-frequency band signals, created 10 GRNN neural networks to categorize feature files as normal or preseizure, and then applied post-processing techniques with 10 thresholding mechanisms to each classifier output. As such, we determined that seizure forewarning may function better in various sub-frequency bands for many patients in a subject-specific manner. We also found that epileptic-seizure forecast performed superior at ‘60Hz high pass’ filtered sub-frequency band EEG signal for all subjects or canines data.