Non-harmonicity in high-frequency components of the intra-operative corticogram to delineate epileptogenic tissue during surgery. Issue 1 (January 2017)

• Record Type: Journal Article
• Authors: Geertsema, Evelien E.; van 't Klooster, Maryse A.; van Klink, Nicole E.C.; Leijten, Frans S.S.; van Rijen, Peter C.; Visser, Gerhard H.; Kalitzin, Stiliyan N.; Zijlmans, Maeike
• Is Part Of: Clinical neurophysiology. Volume 128:Issue 1(2017:Jan.)
• Page Start: 153
• Page End: 164
• Publication Date: 2017-01
• Subjects: ARRm autoregressive model residual modulation (novel algorithm) -- ARRmorig autoregressive model residual modulation (original algorithm) -- ECoG electrocorticogram -- FR fast ripples -- HFO high-frequency oscillation -- R ripples -- rn residual signal variance, after modelling using an autoregressive model with order n -- S spikes; Epilepsy surgery -- Post-surgical outcome -- Automatic localisation -- High-frequency oscillations -- Non-harmonicity; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2016.11.007
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: An auto-regressive model residual modulation (ARRm) delineates epileptic tissue based on the non-harmonicity of the signal. Tissue with high ARRm corresponds to fast ripples and removal of this tissue relates to outcome. The ARRm can be used as real-time analysis tool during epilepsy surgery as it is fast and insensitive to subtle artefacts. Abstract: Objective: We aimed to test the potential of auto-regressive model residual modulation (ARRm), an artefact-insensitive method based on non-harmonicity of the high-frequency signal, to identify epileptogenic tissue during surgery. Methods: Intra-operative electrocorticography (ECoG) of 54 patients with refractory focal epilepsy were recorded pre- and post-resection at 2048 Hz. The ARRm was calculated in one-minute epochs in which high-frequency oscillations (HFOs; fast ripples, 250–500 Hz; ripples, 80–250 Hz) and spikes were marked. We investigated the pre-resection fraction of HFOs and spikes explained by the ARRm ( h 2 -index). A general ARRm threshold was set and used to compare the ARRm to surgical outcome in post-resection ECoG (Pearson X 2 ). Results: ARRm was associated strongest with the number of fast ripples in pre-resection ECoG ( h 2 = 0.80, P < 0.01), but also with ripples and spikes. An ARRm threshold of 0.47 yielded high specificity (95%) with 52% sensitivity for channels with fast ripples. ARRm values >0.47 were associated with poor outcome at channel and patient level (both P < 0.01) in post-resection ECoG. Conclusions: The ARRm algorithm might enable intra-operative delineation of epileptogenic tissue. Significance: ARRm is the first unsupervised real-time analysis that could provide an intra-operative, 'on demand' interpretation per electrode about the need to remove underlying tissue to optimize the chance of seizure freedom.