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Visually validated semi-automatic high-frequency oscillation detection aides the delineation of epileptogenic regions during intra-operative electrocorticography
Abstract
OBJECTIVE: To test the utility of a novel semi-automated method for detecting, validating, and quantifying high-frequency oscillations (HFOs): ripples (80-200 Hz) and fast ripples (200-600 Hz) in intra-operative electrocorticography (ECoG) recordings. METHODS: Sixteen adult patients with temporal lobe epilepsy (TLE) had intra-operative ECoG recordings at the time of resection. The computer-annotated ECoG recordings were visually inspected and false positive detections were removed. We retrospectively determined the sensitivity, specificity, positive and negative predictive value (PPV/NPV) of HFO detections in unresected regions for determining post-operative seizure outcome. RESULTS: Visual validation revealed that 2.81% of ripple and 43.68% of fast ripple detections were false positive. Inter-reader agreement for false positive fast ripple on spike classification was good (ICC = 0.713, 95% CI: 0.632-0.779). After removing false positive detections, the PPV of a single fast ripple on spike in an unresected electrode site for post-operative non-seizure free outcome was 85.7 [50-100%]. Including false positive detections reduced the PPV to 64.2 [57.8-69.83%]. CONCLUSIONS: Applying automated HFO methods to intraoperative electrocorticography recordings results in false positive fast ripple detections. True fast ripples on spikes are rare, but predict non-seizure free post-operative outcome if found in an unresected site. SIGNIFICANCE: Semi-automated HFO detection methods are required to accurately identify fast ripple events in intra-operative ECoG recordings.
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Authors (18)
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Shennan Aibel Weiss
- Thomas Jefferson University, Philadelphia Dept. of Neurology and Neuroscience
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Brent Berry
- Mayo Systems Electrophysiology Laboratory (MSEL) Department of Neurology
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Inna Chervoneva
- Thomas Jefferson University Dept. of Pharmacology & Experimental Therapeutics
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Zachary Waldman
- Thomas Jefferson University, Philadelphia Dept. of Neurology and Neuroscience
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Jonathan Guba
- Thomas Jefferson University, Philadelphia Dept. of Neurology and Neuroscience
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Mark R. Bower
- Mayo Systems Electrophysiology Laboratory Department of Neurology
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Benjamin Brinkmann
- Mayo Systems Electrophysiology Laboratory Department of Neurology
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Vaclav Kremen
- Mayo Systems Electrophysiology Laboratory Department of Neurology
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Fatemeh Khadjevand
- Mayo Systems Electrophysiology Laboratory Department of Neurology
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Yogatheesan Varatharajah
- Mayo Systems Electrophysiology Laboratory Department of Neurology
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Hari Guragain
- Mayo Systems Electrophysiology Laboratory Department of Neurology
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Ashwini Sharan
- Dept. of Neurosurgery, Thomas Jefferson University, Dept. of Neurosurgery, Thomas Jefferson University,
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Chengyuan Wu
- Thomas Jefferson University Dept. of Neurosurgery
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Richard Staba
- Dept. of Neurology, University of California Los Angeles Dept. of Neurology, University of California Los Angeles
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Jerome Engel Jr.
- University of California Los Angeles Dept. of Neurology
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Michael R. Sperling
- Thomas Jefferson University Dept. of Neurology
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Gregory Worrell
- Mayo Systems Electrophysiology Laboratory Department of Neurology
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Details
- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
-
Clinical Neurophysiology
no. 129,
edition 10,
pages 2089 - 2098,
ISSN: 1388-2457 - Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Weiss S., Berry B., Chervoneva I., Waldman Z., Guba J., Bower M., Kucewicz M., Brinkmann B., Kremen V., Khadjevand F., Varatharajah Y., Guragain H., Sharan A., Wu C., Staba R., Engel Jr. J., Sperling M., Worrell G.: Visually validated semi-automatic high-frequency oscillation detection aides the delineation of epileptogenic regions during intra-operative electrocorticography// Clinical Neurophysiology. -Vol. 129, iss. 10 (2018), s.2089-2098
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.clinph.2018.06.030
- Verified by:
- Gdańsk University of Technology
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