Detection of Epileptic Seizures in Long Eeg Recordings Using an Anomaly Detector with Artifact Rejection

Abstract

Manual seizure detection from long recordings of the electroencephalogram (EEG) is a tiring, tedious, and error-prone process. It also requires experienced practitioners to detect seizure events precisely. This paper has proposed a novel method to detect epileptic seizures from long-EEG recordings using some state-of-the-art anomaly detectors and artifact rejection techniques. This method has two stages: 1) pre-screened EEGs for potential seizure detection and 2) seizure detection by artifact removal. For Stage 1, we have experimented with six methods of well-known anomaly detection. In Stage 2, we have proposed some artifact removal techniques to separate artifacts from seizure events based on the features calculated from the statistical properties of EEG. The proposed method has been evaluated with a private EEG dataset from Juntendo University Medical School, Japan. The proposed approach performed quite well with sensitivity (86–89%), specificity (92–94%), precision (20–22%), and seizure event detection ratio (89–91%). The promising results of the method encourage further improvement in the near future.