A method for the topographical identification and quantification of high frequency oscillations in intracranial electroencephalography recordings

Zachary J. Waldman, Shoichi Shimamoto, Inkyung Song, Iren Orosz, Anatol Bragin, Itzhak Fried, Jerome Engel, Richard Staba, Michael R. Sperling, Shennan A. Weiss*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


Objective To develop a reliable software method using a topographic analysis of time-frequency plots to distinguish ripple (80–200 Hz) oscillations that are often associated with EEG sharp waves or spikes (RonS) from sinusoid-like waveforms that appear as ripples but correspond with digital filtering of sharp transients contained in the wide bandwidth EEG. Methods A custom algorithm distinguished true from false ripples in one second intracranial EEG (iEEG) recordings using wavelet convolution, identifying contours of isopower, and categorizing these contours into sets of open or closed loop groups. The spectral and temporal features of candidate groups were used to classify the ripple, and determine its duration, frequency, and power. Verification of detector accuracy was performed on the basis of simulations, and visual inspection of the original and band-pass filtered signals. Results The detector could distinguish simulated true from false ripple on spikes (RonS). Among 2934 visually verified trials of iEEG recordings and spectrograms exhibiting RonS the accuracy of the detector was 88.5% with a sensitivity of 81.8% and a specificity of 95.2%. The precision was 94.5% and the negative predictive value was 84.0% (N = 12). Among, 1,370 trials of iEEG recording exhibiting RonS that were reviewed blindly without spectrograms the accuracy of the detector was 68.0%, with kappa equal to 0.01 ± 0.03. The detector successfully distinguished ripple from high spectral frequency ‘fast ripple’ oscillations (200–600 Hz), and characterize ripple duration and spectral frequency and power. The detector was confounded by brief bursts of gamma (30–80 Hz) activity in 7.31 ± 6.09% of trials, and in 30.2 ± 14.4% of the true RonS detections ripple duration was underestimated. Conclusions Characterizing the topographic features of a time-frequency plot generated by wavelet convolution is useful for distinguishing true oscillations from false oscillations generated by filter ringing. Significance Categorizing ripple oscillations and characterizing their properties can improve the clinical utility of the biomarker.

Original languageEnglish
Pages (from-to)308-318
Number of pages11
JournalClinical Neurophysiology
Issue number1
StatePublished - Jan 2018
Externally publishedYes


FundersFunder number
National Institute of Neurological Disorders and StrokeR01NS065877


    • Filter ringing
    • High-frequency oscillation
    • Ripple
    • Topography
    • Wavelet


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