Automatic Detection of Ictal Activity in EEG Channels using Synchronization Attributes

To aid doctors with the process of scanning long-term Electroencephalogram (EEG) records for seizure detection, automatic seizure detectors (ASD) have been proposed. ASD have many clinical uses and have gained much recent interest in the past few decades. However, due to high computational loads of the ASD algorithms; their real-time application has been hindered. The aim of this work is to build a detector that has low complexity and can detect seizures with high sensitivity and minimum latency. To this end, this paper proposes a patient-specific seizure onset detector that uses a single feature based on neural synchrony as it has been shown that once the brain approaches ictal activity, neural activity becomes less chaotic and more synchronous. Leveraging on this phenomena, the condition number is utilized to measure the synchronization between EEG channels. The developed detector has three main stages: preprocessing, condition number calculation and classification. After filtering an input EEG record, the condition number of an EEG window is computed and fed into a classifier that determines whether the current window is normal or abnormal. Classification was done by two forms of a support vector machine (SVM) classifier; namely, binary SVM and a one-class SVM. The developed detector was evaluated using the CHB-MIT dataset and was evaluated using 10 patients from this dataset. The proposed detector achieved a sensitivity of 97% and a maximum false positive rate of five false alarms per hour. The performance of the detector is comparable to seizure detectors that use multiple features; but lightweight for use for real-time processing of EEG data.