Amir G Baroumand1, Pieter van Mierlo1, Gregor Strobbe2, Lars H Pinborg3, Martin Fabricius4, Guido Rubboli5, Anne-Mette Leffers6, Peter Uldall7, Bo Jespersen8, Jannick Brennum8, Otto Mølby Henriksen9, Sándor Beniczky10. 1. Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University - imec, De Pintelaan 185, 9000 Ghent, Belgium. 2. Epilog, Vlasgaardstraat 52, 9000 Ghent, Belgium. 3. Department of Neurology, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen, Denmark; Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, 9 Blegdamsvej, DK-2100 Copenhagen, Denmark. 4. Department of Clinical Neurophysiology, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen, Denmark. 5. Department of Neurology, Danish Epilepsy Centre, Kolonivej 1, 4293 Dianalund, Denmark. 6. Department of Diagnostic Radiology, Hvidovre Hospital, Kettegaard Alle 30, 2650 Hvidovre, Denmark. 7. Department of Paediatrics, Child Neurology, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen, Denmark. 8. Department of Neurosurgery, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen, Denmark. 9. Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen, Denmark. 10. Department of Clinical Neurophysiology, Danish Epilepsy Centre, Visby Allé 5, 4293 Dianalund, Denmark; Department of Clinical Neurophysiology, Aarhus University Hospital, Noerrebrogade 44, 8000 Aarhus, Denmark. Electronic address: sbz@filadelfia.dk.
Abstract
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.
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.
Authors: Lorenzo Ricci; Eleonora Tamilia; Michel Alhilani; Aliza Alter; Μ Scott Perry; Joseph R Madsen; Jurriaan M Peters; Phillip L Pearl; Christos Papadelis Journal: Clin Neurophysiol Date: 2021-04-28 Impact factor: 4.861
Authors: Shuai Ye; Lin Yang; Yunfeng Lu; Michal T Kucewicz; Benjamin Brinkmann; Cindy Nelson; Abbas Sohrabpour; Gregory A Worrell; Bin He Journal: Neurology Date: 2020-10-23 Impact factor: 9.910