An Unsupervised Methodology for the Detection of Epileptic Seizures Using EEG Signals: A Multi-Dataset Evaluation.

Although the electroencephalogram (EEG) is the most commonly used means to monitor epileptic patients, public EEG datasets are very scarce making it difficult to develop and validate seizure detection algorithms. In this work an unsupervised seizure detection methodology is used to isolate ictal EEG segments without requiring any apriori information or human intervention. Seizures are detected using four simple seizure detection conditions that are activated when rhythmical activity from different brain areas is simultaneously concentrated in the alpha (8-13 Hz), theta (4-7 Hz) or delta (1-3 Hz) frequency range. Then, only a small proportion of the EEG segments that are most likely to contain ictal activity is selected and presented to the physician for the final evaluation. In this way, large volumes of EEG signals can be annotated in a fraction of the time and effort that would be otherwise required. Using EEG data from 33 sessions from the Temple University Hospital (TUH) EEG Corpus, our unsupervised methodology reached, on average, 84.92% seizure detection sensitivity with 3.46 false detections per hour of EEG signals.