Spatiotemporal analysis of interictal EEG for automated seizure detection and classification

ObjectiveSeizure type classification is important as therapy differs for different epilepsy subtypes. Currently, skilled neurologists classify seizures based on visual analysis. However, manual EEG inspection is time-consuming, laborious, subjective, and prone to misclassification due to artifacts and EEG variability. This work aims to address these limitations.MethodsIn this work, a quick, robust, and accurate spatiotemporal analytical algorithm is developed to classify epileptic seizures. The EEG data set is sampled at 125 Hz using a Nicolet EEG system. Robust preprocessing, feature extraction, and optimal classifiers captured IEDs (Interictal Epileptiform Discharges), including spikes, sharps, slow waves, and Spike-Wave Discharges (SWD).ResultsThe developed classifier results are validated against clinical impressions provided by experienced epileptologists. The algorithm automatically classifies the EEG data into four types: normal, focal, generalized, and absence, with 93.18 % accuracy (n = 88).ConclusionThe results suggest a novel way to screen epileptic subjects without false positives (accuracy: 94.32 %, n = 88) and tentatively identify the seizure type. Blind validation further confirms the generalizability of the classifier (accuracy: 90.90 %, n = 11).SignificanceThe developed algorithm reduces the workload of neurologists for epilepsy screening and identifies seizure onset zone, temporal spread, and overall scalp distribution of epileptic activities.

Automated summary

This study developed a quick, robust, and accurate algorithm to classify epileptic seizures, reducing the workload of neurologists for epilepsy screening and identifying important features of epileptic activities.