An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard. Issue 6 (June 2020)

• Record Type: Journal Article
• Authors: Fürbass, Franz; Kural, Mustafa Aykut; Gritsch, Gerhard; Hartmann, Manfred; Kluge, Tilmann; Beniczky, Sándor
• Is Part Of: Clinical neurophysiology. Volume 131:Issue 6(2020:Jun.)
• Page Start: 1174
• Page End: 1179
• Publication Date: 2020-06
• Subjects: Automatic spike detection -- Biomarker -- Deep learning -- EEG -- Interictal epileptiform discharges -- Epilepsy; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2020.02.032
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: We developed an algorithm for automatic detection of epileptiform EEG discharges, based on a novel deep learning method. We validated it against the diagnostic gold standard from video-EEG recordings of the seizures. The sensitivity of the algorithm was 89% and the specificity was 70%. Abstract: Objective: To validate an artificial intelligence-based computer algorithm for detection of epileptiform EEG discharges (EDs) and subsequent identification of patients with epilepsy. Methods: We developed an algorithm for automatic detection of EDs, based on a novel deep learning method that requires a low amount of labeled EEG data for training. Detected EDs are automatically grouped into clusters, consisting of the same type of EDs, for rapid visual inspection. We validated the algorithm on an independent dataset of 100 patients with sharp transients in their EEG recordings (54 with epilepsy and 46 with non-epileptic paroxysmal events). The diagnostic gold standard was derived from the video-EEG recordings of the patients' habitual events. Results: The algorithm had a sensitivity of 89% for identifying EEGs with EDs recorded from patients with epilepsy, a specificity of 70%, and an overall accuracy of 80%. Conclusions: Automated detection of EDs using an artificial intelligence-based computer algorithm had a high sensitivity. Human (expert) supervision is still necessary for confirming the clusters of detected EDs and for describing clinical correlations. Further studies on different patient populations will be needed to confirm our results. Significance: The automated algorithm we describe here is a useful tool, assisting neurophysiologist in rapid assessment of EEG recordings.