ABSTRACT Epilepsy is one of the most common neurological disorders, which manifests as unprovoked seizures. The prevalence of epilepsy is higher in developing countries, where medical facilities are illequipped and under-staffed. Mobile EEG devices promise a new dawn for long-term ambulatory EEG monitoring, which has a potential to revolutionize health care for neurological disorders especially epilepsy. Increasing the outreach to underserved communities and continuous monitoring of patients will yield vast amount of data. This requires the development of a method that can mark regions of interest, to aid in the evaluation of the EEG trial by the experts. Such an experimental setting calls for an unsupervised method, which can detect seizure regions with high accuracy. This paper focuses on the development of a seizure detection method with the above-stated characteristics. Group invariant scattering, a novel data representation technique, has been used for feature extraction. Tested on CHB-MIT data set, the proposed methodology outperforms the current state-of-the-art approaches under similar testing conditions, by successfully detecting 180 out of 197 seizures. INDEX TERMS Biomedical signal processing, wavelet transforms, signal representation, event detection, unsupervised learning.
A recent unsupervised technique uses group invariant scattering for feature extraction from the EEG data and proposes an anomaly-detection based algorithm for identifying seizure regions REF .
Mallat’s Scattering Transform Based Anomaly Sensing for Detection of Seizures in Scalp EEG
IEEE Access
IEEE Access
Computer Science