The CS algorithm: A novel method for high frequency oscillation detection in EEG

• Published in: Journal of neuroscience methods Vol. 293; pp. 6 - 16
• Main Authors: Cimbálník, Jan, Hewitt, Angela, Worrell, Greg, Stead, Matt
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2018
• Subjects: Algorithms; Animals; Brain - physiology; Brain - physiopathology; Detection algorithm; Dogs; Electroencephalography - methods; Epilepsy - diagnosis; Epilepsy - physiopathology; False Positive Reactions; Frequency dominance; HFO; High frequency oscillations; Humans; Ripples; ROC Curve; Rodentia; Signal Processing, Computer-Assisted; Time Factors; HFO; High frequency oscillations; Ripples; Detection algorithm; Frequency dominance
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2017.08.023

•A novel method for detecting High Frequency Oscillations is presented and validated.•Sensitivity and specificity are shown to be superior to established algorithms.•Performance is shown to be sufficient for unsupervised use in a clinical setting. High frequency oscillations (HFOs) are emerging as potentially clinically important biomarkers for localizing seizure generating regions in epileptic brain. These events, however, are too frequent, and occur on too small a time scale to be identified quickly or reliably by human reviewers. Many of the deficiencies of the HFO detection algorithms published to date are addressed by the CS algorithm presented here. The algorithm employs novel methods for: 1) normalization; 2) storage of parameters to model human expertise; 3) differentiating highly localized oscillations from filtering phenomena; and 4) defining temporal extents of detected events. Receiver-operator characteristic curves demonstrate very low false positive rates with concomitantly high true positive rates over a large range of detector thresholds. The temporal resolution is shown to be +/−∼5ms for event boundaries. Computational efficiency is sufficient for use in a clinical setting. The algorithm performance is directly compared to two established algorithms by Staba (2002) and Gardner (2007). Comparison with all published algorithms is beyond the scope of this work, but the features of all are discussed. All code and example data sets are freely available. The algorithm is shown to have high sensitivity and specificity for HFOs, be robust to common forms of artifact in EEG, and have performance adequate for use in a clinical setting.