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

Abstract

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.

Bimodal coupling of ripples and slower oscillations during sleep in patients with focal epilepsy

“…Ripples on spikes can arise due to Gibb’s phenomenon as a result of high-pass filtering sharp transients, such as an epileptiform spike 22 . To distinguish authentic/true ripple events that occur during spikes from spurious/false ripple events due to filter ringing, we developed a custom algorithm using a topographic analysis of time-frequency plots 23 (Supporting information). …”

Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively

“…In this study we used a new topographic method (Waldman et al, 2018) to separate true ripple on spikes from false ripple on spikes that result from filtering inter-ictal discharges (Bénar et al, 2010). In accord with other published literature (Burnos et al, 2016), both true and false ripples on spike rates were equivalently increased in the SOZ as compared with the NSOZ.…”

“…The one second iEEG trials containing ripple events as determined by the detection algorithm underwent further processing by a custom automated algorithm that distinguished true from false ripple events using wavelet convolution, identifying contours of isopower, and then categorizing these contours into sets of open or closed loop groups (Waldman et al, 2018). This custom algorithm has been previously validated using simulated data and by visual inspection of iEEG data.…”

“…Simulations were used to measure the stability and reliability of the measurements of ripple properties on the basis of the topographical analysis (Waldman et al, 2018). …”

Unsupervised Detection of High-Frequency Oscillations Using Time-Frequency Maps and Computer Vision

“…These studies identified HFOs in healthy subjects or in epileptic patients performing visual or motor tasks (Matsumoto et al, 2013), while other studies found an increased number of HFOs in brain regions that are part of the epileptogenic network; therefore, the HFO was considered to be a potential epilepsy biomarker (Urrestarazu et al, 2007;Jacobs et al, 2009;Brázdil et al, 2010;Kerber et al, 2013;Geertsema et al, 2015). However, in spite of the various existing models of HFO generation (Fink et al, 2015;Helling et al, 2015), it is still a matter of debate how to distinguish physiological and pathological HFOs (Engel et al, 2009;Waldman et al, 2018).…”