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

Abstract

Objective To develop and validate a detector that identifies ripple (80–200 Hz) events in intracranial EEG (iEEG) recordings in a referential montage and utilizes independent component analysis (ICA) to eliminate or reduce high-frequency artifact contamination. Also, investigate the correspondence of detected ripples and the seizure onset zone (SOZ). Methods iEEG recordings from 16 patients were first band-pass filtered (80–600 Hz) and Infomax ICA was next applied to derive the first independent component (IC1). IC1 was subsequently pruned, and an artifact index was derived to reduce the identification of high-frequency events introduced by the reference electrode signal. A Hilbert detector identified ripple events in the processed iEEG recordings using amplitude and duration criteria. The identified ripple events were further classified and characterized as true or false ripple on spikes, or ripples on oscillations by utilizing a topographical analysis to their time-frequency plot, and confirmed by visual inspection. Results The signal to noise ratio was improved by pruning IC1. The precision of the detector for ripple events was 91.27 ± 4.3%, and the sensitivity of the detector was 79.4 ± 3.0% (N = 16 patients, 5842 ripple events). The sensitivity and precision of the detector was equivalent in iEEG recordings obtained during sleep or intra-operatively. Across all the patients, true ripple on spike rates and also the rates of false ripple on spikes, that were generated due to filter ringing, classified the seizure onset zone (SOZ) with an area under the receiver operating curve (AUROC) of >76%. The magnitude and spectral content of true ripple on spikes generated in the SOZ was distinct as compared with the ripples generated in the NSOZ (p < .001). Conclusions Utilizing ICA to analyze iEEG recordings in referential montage provides many benefits to the study of high-frequency oscillations. The ripple rates and properties defined using this approach may accurately delineate the seizure onset zone. Significance Strategies to improve the spatial resolution of intracranial EEG and reduce artifact can help improve the clinical utility of HFO biomarkers.

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

“…One-second trials of ripples occurring on inter-ictal discharges were identified using a previously described algorithm (Weiss et al, 2016b; Shimamoto et al, 2018). In brief, (1) INFOMAX independent component analysis (Bell and Sejnowski, 1997) was applied to referential recordings to reduce muscle contamination, and demarcate artefactual ripple events produced by muscle contamination (2) ripples were detected using a Hilbert detector applied to the band-pass filtered and ICA processed signal, (3) for each ripple detected a one-second trial was generated with a ripple centered at 0.5 s, (4) To distinguish ripples that occur during epileptiform spikes from all other ripples, we utilized a validated method (Shimamoto et al, 2018).…”

“…In brief, (1) INFOMAX independent component analysis (Bell and Sejnowski, 1997) was applied to referential recordings to reduce muscle contamination, and demarcate artefactual ripple events produced by muscle contamination (2) ripples were detected using a Hilbert detector applied to the band-pass filtered and ICA processed signal, (3) for each ripple detected a one-second trial was generated with a ripple centered at 0.5 s, (4) To distinguish ripples that occur during epileptiform spikes from all other ripples, we utilized a validated method (Shimamoto et al, 2018). We calculated the derivative of the peri-ripple band-pass filtered (4–30 Hz) iEEG and applied a threshold of 4 μV/msec.…”

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

“…Subsequent processing steps for those recordings from macroelectrodes deemed suitable on the basis of visual inspection using BESA v5 (Gräfelfing, Germany) were performed using custom software developed in Matlab. Muscle and electrode artifacts in iEEG recordings from were reduced using a custom independent component analysis (ICA)-based algorithm 20,21 . After applying this ICA-based method, ripples were detected in the referential montage iEEG recordings per contact by utilizing a Hilbert detector 20 , in which (i) applied a 1000th order symmetric finite impulse response (FIR) band-pass filter (80–600 Hz), and (ii) applied Hilbert transform to calculate the instantaneous amplitude of this time series according to the analytic signal z(t), described in Eqn.…”

Nonictal EEG biomarkers for diagnosis and treatment

“…Given that pHFOs have largely been identified by direct brain recording, the majority of studies confirming their reliability as a biomarker of epilepsy have been limited to identification of the epileptogenic region for surgery, which is discussed in detail in the next chapter (J. Wu this issue). Various aspects of HFOs, such as their occurrence on spikes and relationship to slow waves, have been used to distinguish pHFOs from normal HFOs, and novel approaches are utilized to identify false HFOs, and to improve automatic detection . Most importantly, however, for the future clinical application of pHFOs to diagnosis and treatment, and to predict patients at risk for epilepsy, work is being done to identify pHFOs noninvasively.…”

“…Various aspects of HFOs, such as their occurrence on spikes and relationship to slow waves, have been used to distinguish pHFOs from normal HFOs, and novel approaches are utilized to identify false HFOs, and to improve automatic detection. [22][23][24][25][26][27] Most importantly, however, for the future clinical application of pHFOs to diagnosis and treatment, and to predict patients at risk for epilepsy, work is being done to identify pHFOs noninvasively. Several studies have now reported the ability to record activity in the HFO frequency range from scalp electrodes, [28][29][30] but it is unclear whether these events are the same as those recorded directly from the brain.…”