Automated EEG source imaging: A retrospective, blinded clinical validation study

TY - JOUR

T1 - Automated EEG source imaging

T2 - A retrospective, blinded clinical validation study

AU - Baroumand, Amir G.

AU - van Mierlo, Pieter

AU - Strobbe, Gregor

AU - Pinborg, Lars H.

AU - Fabricius, Martin

AU - Rubboli, Guido

AU - Leffers, Anne Mette

AU - Uldall, Peter

AU - Jespersen, Bo

AU - Brennum, Jannick

AU - Henriksen, Otto Mølby

AU - Beniczky, Sándor

PY - 2018

Y1 - 2018

N2 - Objective: To evaluate the accuracy of automated EEG source imaging (ESI) in localizing epileptogenic zone. Methods: Long-term EEG, recorded with the standard 25-electrode array of the IFCN, from 41 consecutive patients with focal epilepsy who underwent resective surgery, were analyzed blinded to the surgical outcome. The automated analysis comprised spike-detection, clustering and source imaging at the half-rising time and at the peak of each spike-cluster, using individual head-models with six tissue-layers and a distributed source model (sLORETA). The fully automated approach presented ESI of the cluster with the highest number of spikes, at the half-rising time. In addition, a physician involved in the presurgical evaluation of the patients, evaluated the automated ESI results (up to four clusters per patient) in clinical context and selected the dominant cluster and the analysis time-point (semi-automated approach). The reference standard was location of the resected area and outcome one year after operation. Results: Accuracy was 61% (95% CI: 45–76%) for the fully automated approach and 78% (95% CI: 62–89%) for the semi-automated approach. Conclusion: Automated ESI has an accuracy similar to previously reported neuroimaging methods. Significance: Automated ESI will contribute to increased utilization of source imaging in the presurgical evaluation of patients with epilepsy.

AB - Objective: To evaluate the accuracy of automated EEG source imaging (ESI) in localizing epileptogenic zone. Methods: Long-term EEG, recorded with the standard 25-electrode array of the IFCN, from 41 consecutive patients with focal epilepsy who underwent resective surgery, were analyzed blinded to the surgical outcome. The automated analysis comprised spike-detection, clustering and source imaging at the half-rising time and at the peak of each spike-cluster, using individual head-models with six tissue-layers and a distributed source model (sLORETA). The fully automated approach presented ESI of the cluster with the highest number of spikes, at the half-rising time. In addition, a physician involved in the presurgical evaluation of the patients, evaluated the automated ESI results (up to four clusters per patient) in clinical context and selected the dominant cluster and the analysis time-point (semi-automated approach). The reference standard was location of the resected area and outcome one year after operation. Results: Accuracy was 61% (95% CI: 45–76%) for the fully automated approach and 78% (95% CI: 62–89%) for the semi-automated approach. Conclusion: Automated ESI has an accuracy similar to previously reported neuroimaging methods. Significance: Automated ESI will contribute to increased utilization of source imaging in the presurgical evaluation of patients with epilepsy.

KW - Automation

KW - EEG

KW - Epilepsy

KW - Presurgical evaluation

KW - Source imaging

KW - Source localization

U2 - 10.1016/j.clinph.2018.09.015

DO - 10.1016/j.clinph.2018.09.015

M3 - Journal article

C2 - 30278389

AN - SCOPUS:85054094650

SN - 1388-2457

VL - 129

SP - 2403

EP - 2410

JO - Clinical Neurophysiology

JF - Clinical Neurophysiology

IS - 11

ER -