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High inter-reviewer variability of spike detection on intracranial EEG addressed by an automated multi-channel algorithm.

ABSTRACT: The goal of this study was to determine the consistency of human reviewer spike detection and then develop a computer algorithm to make the intracranial spike detection process more objective and reliable.Three human reviewers marked interictal spikes on samples of intracranial EEGs from 10 patients. The sensitivity, precision and agreement in channel ranking by activity were calculated between reviewers. A computer algorithm was developed to parallel the way human reviewers detect spikes by first identifying all potential spikes on each channel using frequency filtering and then block scaling all channels at the same time in order to exclude potential spikes that fall below an amplitude and slope threshold. Its performance was compared to the human reviewers on the same set of patients.Human reviewers showed surprisingly poor inter-reviewer agreement, but did broadly agree on the ranking of channels for spike activity. The computer algorithm performed as well as the human reviewers and did especially well at ranking channels from highest to lowest spike frequency.Our algorithm showed good agreement with the different human reviewers, even though they demonstrated different criteria for what constitutes a 'spike' and performed especially well at the clinically important task of ranking channels by spike activity.An automated, objective method to detect interictal spikes on intracranial recordings will improve both research and the surgical management of epilepsy patients.

SUBMITTER: Barkmeier DT

PROVIDER: S-EPMC3277646 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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High inter-reviewer variability of spike detection on intracranial EEG addressed by an automated multi-channel algorithm.

Barkmeier Daniel T DT Shah Aashit K AK Flanagan Danny D Atkinson Marie D MD Agarwal Rajeev R Fuerst Darren R DR Jafari-Khouzani Kourosh K Loeb Jeffrey A JA

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 20111026 6

<h4>Objective</h4>The goal of this study was to determine the consistency of human reviewer spike detection and then develop a computer algorithm to make the intracranial spike detection process more objective and reliable.<h4>Methods</h4>Three human reviewers marked interictal spikes on samples of intracranial EEGs from 10 patients. The sensitivity, precision and agreement in channel ranking by activity were calculated between reviewers. A computer algorithm was developed to parallel the way hu ...[more]

PMID: 22033028

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Project description:ObjectivesSimultaneous intracranial EEG and functional MRI (icEEG-fMRI) can be used to map the haemodynamic (BOLD) changes associated with the generation of IEDs. Unlike scalp EEG-fMRI, in most patients who undergo icEEG-fMRI, IEDs recorded intracranially are numerous and show variability in terms of field amplitude and morphology. Therefore, visual marking can be highly subjective and time consuming. In this study, we applied an automated spike classification algorithm, Wave_clus (WC), to IEDs marked visually on icEEG data acquired during simultaneous fMRI acquisition. The motivation of this work is to determine whether using a potentially more consistent and unbiased automated approach can produce more biologically meaningful BOLD patterns compared to the BOLD patterns obtained based on the conventional, visual classification.MethodsWe analysed simultaneous icEEG-fMRI data from eight patients with severe drug resistant epilepsy, and who subsequently underwent resective surgery that resulted in a good outcome: confirmed epileptogenic zone (EZ). For each patient two fMRI analyses were performed: one based on the conventional visual IED classification and the other based on the automated classification. We used the concordance of the IED-related BOLD maps with the confirmed EZ as an indication of their biological meaning, which we compared for the automated and visual classifications for all IED originating in the EZ.ResultsAcross the group, the visual and automated classifications resulted in 32 and 24 EZ IED classes respectively, for which 75% vs 83% of the corresponding BOLD maps were concordant. At the single-subject level, the BOLD maps for the automated approach had greater concordance in four patients, and less concordance in one patient, compared to those obtained using the conventional visual classification, and equal concordance for three remaining patients. These differences did not reach statistical significance.ConclusionWe found automated IED classification on icEEG data recorded during fMRI to be feasible and to result in IED-related BOLD maps that may contain similar or greater biological meaning compared to the conventional approach in the majority of the cases studied. We anticipate that this approach will help to gain significant new insights into the brain networks associated with IEDs and in relation to postsurgical outcome.

Dataset Information

High inter-reviewer variability of spike detection on intracranial EEG addressed by an automated multi-channel algorithm.

Publications

High inter-reviewer variability of spike detection on intracranial EEG addressed by an automated multi-channel algorithm.

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