Forskning
Udskriv Udskriv
Switch language
Rigshospitalet - en del af Københavns Universitetshospital
Udgivet

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

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  1. A method to assess the default EEG macrostate and its reactivity to stimulation

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. Automatic continuous EEG signal analysis for diagnosis of delirium in patients with sepsis

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. Early axonal loss predicts long-term disability in chronic inflammatory demyelinating polyneuropathy

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  4. Myelin protein zero gene dose dependent axonal ion-channel dysfunction in a family with Charcot-Marie-Tooth disease

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  5. Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  1. Divergent Cellular Energetics, Glutamate Metabolism, and Mitochondrial Function Between Human and Mouse Cerebral Cortex

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. Serum metabolome associated with severity of acute traumatic brain injury

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. GDNF Increases Inhibitory Synaptic Drive on Principal Neurons in the Hippocampus via Activation of the Ret Pathway

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  4. Deep learning based low-activity PET reconstruction of [11C]PiB and [18F]FE-PE2I in neurodegenerative disorders

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Vis graf over relationer

OBJECTIVE: EEG source imaging (ESI) is a validated tool in the multimodal workup of patients with drug resistant focal epilepsy. However, it requires special expertise and it is underutilized. To circumvent this, automated analysis pipelines have been developed and validated for the interictal discharges. In this study, we present the clinical validation of an automated ESI for ictal EEG signals.

METHODS: We have developed an automated analysis pipeline of ictal EEG activity, based on spectral analysis in source space, using an individual head model of six tissues. The analysis was done blinded to all other data. As reference standard, we used the concordance with the resected area and one-year postoperative outcome.

RESULTS: We analyzed 50 consecutive patients undergoing epilepsy surgery (34 temporal and 16 extra-temporal). Thirty patients (60%) became seizure-free. The accuracy of the automated ESI was 74% (95% confidence interval: 59.66-85.37%).

CONCLUSIONS: Automated ictal ESI has a high accuracy for localizing the seizure onset zone.

SIGNIFICANCE: Automating the ESI of the ictal EEG signals will facilitate implementation of this tool in the presurgical evaluation.

OriginalsprogEngelsk
TidsskriftClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Vol/bind141
Sider (fra-til)119-125
Antal sider7
ISSN1388-2457
DOI
StatusUdgivet - sep. 2022

ID: 65653300