The CS algorithm: A novel method for high frequency oscillation detection in EEG.
The CS algorithm: A novel method for high frequency oscillation detection in EEG.
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DOI:
10.1016/j.jneumeth.2017.08.023
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发表时间:
2018-01-01
影响因子:
3
通讯作者:
Stead M
中科院分区:
文献类型:
--
作者:
Cimbálník J;Hewitt A;Worrell G;Stead M
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 +/−~5 ms for event boundaries. Computational efficiency is sufficient for use in a clinical setting. The algorithm performance is directly compared to two established algorithms by and. 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.
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