Detection of high-frequency oscillations in electroencephalography: A scoping review and an adaptable open-source framework. (January 2021)

• Record Type: Journal Article
• Authors: Wong, Simeon M.; Arski, Olivia N.; Workewych, Adriana M.; Donner, Elizabeth; Ochi, Ayako; Otsubo, Hiroshi; Snead, O. Carter; Ibrahim, George M.
• Is Part Of: Seizure. Volume 84(2021)
• Page Start: 23
• Page End: 33
• Publication Date: 2021-01
• Subjects: Epilepsy -- High frequency oscillations -- Automated detection -- Electroencephalography; Epilepsy -- Periodicals; Epilepsy -- Periodicals; Seizures -- Periodicals; Épilepsie -- Périodiques; Electronic journals; Electronic journals; 616.853
• DOI: 10.1016/j.seizure.2020.11.009
• Languages: English
• ISSNs: 1059-1311

Abstract

Highlights: Current automated HFO detectors are variable in their methodology and require optimization and validation. We provide a comprehensive summary of automated HFO detection methods by performing a scoping review. We propose a framework for defining and detecting HFOs based on a set of user-definable inclusion and exclusion criteria. These findings summarize and advance HFO detection in clinical neurophysiology. Abstract: Purpose: High frequency oscillations (HFOs) are putative biomarkers of epileptogenicity. These electrophysiological phenomena can be effectively detected in electroencephalography using automated methods. Nonetheless, the implementation of these methods into clinical practice remains challenging as significant variability exists between algorithms and their characterizations of HFOs. Here, we perform a scoping review of the literature pertaining to automated HFO detection methods. In addition, we propose a framework for defining and detecting HFOs based on a simplified single-stage time-frequency based detection algorithm with clinically-familiar parameters. Methods: Several databases (OVID Medline, Web of Science, PubMed) were searched for articles presenting novel, automated HFO detection methods. Details related to the algorithm and various stages of data acquisition, pre-processing, and analysis were abstracted from included studies. Results: From the 261 records screened, 57 articles presented novel, automated HFO detection methods and were included in the scoping review. These algorithms were categorized into 3 groups based on their most salient features: energy thresholding, time-frequency analysis, and data mining/machine learning. Algorithms were optimized for specific datasets and suffered from low specificity. A framework for user-constrained inputs is proposed to circumvent some of the weaknesses of highly performant detectors. Conclusions: Further efforts are required to optimize and validate existing automated HFO detection methods for clinical utility. The proposed framework may be applied to understand and standardize the variations in HFO definitions across institutions.