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A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram.

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Title: A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram.
Authors: Chu, Catherine J.1 cjchu@mgh.harvard.edu, Chan, Arthur2, Song, Dan1, Staley, Kevin J.1, Stufflebeam, Steven M.3,4, Kramer, Mark A.5
Source: Journal of Neuroscience Methods. Feb2017, Vol. 277, p46-55. 10p.
Subjects: Electroencephalography, Ripple (Computer network protocol), Visual perception, Biomarkers, Oscillations
Abstract: Background High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. New method The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. Results We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. Comparison with existing method The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Conclusions Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 120797788
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
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  Data: A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram.
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  Data: <searchLink fieldCode="AR" term="%22Chu%2C+Catherine+J%2E%22">Chu, Catherine J.</searchLink><relatesTo>1</relatesTo><i> cjchu@mgh.harvard.edu</i><br /><searchLink fieldCode="AR" term="%22Chan%2C+Arthur%22">Chan, Arthur</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Song%2C+Dan%22">Song, Dan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Staley%2C+Kevin+J%2E%22">Staley, Kevin J.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Stufflebeam%2C+Steven+M%2E%22">Stufflebeam, Steven M.</searchLink><relatesTo>3,4</relatesTo><br /><searchLink fieldCode="AR" term="%22Kramer%2C+Mark+A%2E%22">Kramer, Mark A.</searchLink><relatesTo>5</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Neuroscience+Methods%22">Journal of Neuroscience Methods</searchLink>. Feb2017, Vol. 277, p46-55. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Ripple+%28Computer+network+protocol%29%22">Ripple (Computer network protocol)</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+perception%22">Visual perception</searchLink><br /><searchLink fieldCode="DE" term="%22Biomarkers%22">Biomarkers</searchLink><br /><searchLink fieldCode="DE" term="%22Oscillations%22">Oscillations</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Background High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. New method The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. Results We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. Comparison with existing method The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Conclusions Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Feb2017
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