Epileptic Seizure Detection from EEG Data Using the Active Threshold Method

Abstract: Epilepsy, defined by the WHO as a "chronic brain disease," affects approximately 0.8-1.0% of the world's population, with an estimated 1 million patients in Japan.While advances in machine learning and deep learning have improved the accuracy of epilepsy detection in recent years, high computational costs limit real-time processing. In this study, we investigated the feasibility of applying the Active Threshold (AT) method, developed for real-time voluntary eye movement detection using electrooculography (EOG), to epilepsy detection. The AT method calculates the root mean square (RMS) value from biosignals and multiplies it by an arbitrary parameter, α, to determine the threshold. This method has the advantages of real-time processing and easy calibration.In this study, we applied the AT method to electroencephalogram (EEG) data, including epileptic seizures, released by Boston Children's Hospital to verify whether an appropriate threshold could be derived. In particular, we performed a detailed analysis of the effect of changes in the α value on epilepsy detection accuracy. We selected the records of subject CHB-01 from the dataset and used preprocessed data totaling 7 hours, including epileptic symptoms. The α value was varied from 7 to 10, and the RMS calculation time was fixed at 30 seconds. In the detection evaluation, detections within the range of the epileptic seizure duration recorded in the dataset plus the 30-second RMS calculation time were considered positive, and detections outside this range were considered false.As a result, epileptic seizures were detected across all tested α parameters. However, certain seizure events within the seven-hour dataset could not be detected using any of the parameter values. These undetected seizures exhibited gradual EEG amplitude changes without significant potential amplification compared to interictal periods, making them undetectable by the AT method's approach. Additionally, noise-induced artifacts were erroneously classified as seizure events, resulting in false positive detections. Future work will need to incorporate seizure classification algorithms to distinguish genuine epileptic activity from noise artifacts in the detected EEG signals.

Keywords: EEG, Epileptic Seizure, Threshold

DOI: 10.54941/ahfe1006983

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