Visually validated semi-automatic high-frequency oscillation detection aides the delineation of epileptogenic regions during intra-operative electrocorticography

Publication date: Available online 20 July 2018

Source: Clinical Neurophysiology

Author(s): Shennan Aibel Weiss, Brent Berry, Inna Chervoneva, Zachary Waldman, Jonathan Guba, Mark Bower, Michal Kucewicz, Benjamin Brinkmann, Vaclav Kremen, Fatemeh Khadjevand, Yogatheesan Varatharajah, Hari Guragain, Ashwini Sharan, Chengyuan Wu, Richard Staba, Jerome Engel, Michael Sperling, Gregory Worrell

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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