Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/34243
Title: Comparison of Automated Spike Detection Software in Detecting Epileptiform Abnormalities on Scalp-EEG of Genetic Generalized Epilepsy Patients.
Austin Authors: Janmohamed, Mubeen;Nhu, Duong;Shakathreh, Lubna;Gonen, Ofer;Kuhlman, Levin;Gilligan, Amanda K ;Tan, Chang Wei;Perucca, Piero ;O'Brien, Terence J;Kwan, Patrick
Affiliation: Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.;Department of Neurology, Alfred Health, Melbourne, Victoria, Australia.;Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia.
Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia.
Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia.
Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.
Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia.
Neurosciences Clinical Institute, Epworth Healthcare Hospital, Melbourne, Victoria, Australia.
Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia.
Department of Neurology, Alfred Health, Melbourne, Victoria, Australia.;
Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia.
Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.;Department of Neurology, Alfred Health, Melbourne, Victoria, Australia.
Epilepsy Research Centre
Neurology
Issue Date: 30-Oct-2023
Date: 2023
Publication information: Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society 2023-10-30
Abstract: Despite availability of commercial EEG software for automated epileptiform detection, validation on real-world EEG datasets is lacking. Performance evaluation of two software packages on a large EEG dataset of patients with genetic generalized epilepsy was performed. Three epileptologists labelled IEDs manually of EEGs from three centres. All Interictal epileptiform discharge (IED) markings predicted by two commercial software (Encevis 1.11 and Persyst 14) were reviewed individually to assess for suspicious missed markings and were integrated into the reference standard if overlooked during manual annotation during a second phase. Sensitivity, precision, specificity, and F1-score were used to assess the performance of the software packages against the adjusted reference standard. One hundred and twenty-five routine scalp EEG recordings from different subjects were included (total recording time, 310.7 hours). The total epileptiform discharge reference count was 5,907 (including spikes and fragments). Encevis demonstrated a mean sensitivity for detection of IEDs of 0.46 (SD 0.32), mean precision of 0.37 (SD 0.31), and mean F1-score of 0.43 (SD 0.23). Using the default medium setting, the sensitivity of Persyst was 0.67 (SD 0.31), with a precision of 0.49 (SD 0.33) and F1-score of 0.51 (SD 0.25). Mean specificity representing non-IED window identification and classification was 0.973 (SD 0.08) for Encevis and 0.968 (SD 0.07) for Persyst. Automated software shows a high degree of specificity for detection of nonepileptiform background. Sensitivity and precision for IED detection is lower, but may be acceptable for initial screening in the clinical and research setting. Clinical caution and continuous expert human oversight are recommended with all EEG recordings before a diagnostic interpretation is provided based on the output of the software.
URI: https://ahro.austin.org.au/austinjspui/handle/1/34243
DOI: 10.1097/WNP.0000000000001039
ORCID: 0000-0001-8601-3686
Journal: Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society
PubMed URL: 37934089
ISSN: 1537-1603
Type: Journal Article
Appears in Collections:Journal articles

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