Tagging EEG features within exam reports to quickly generate databases for research purposes

•EEG features classification is useful for clinical and research purposes but can be time-consuming.•EEG report parser is a customizable script to quickly insert and extract feature tags from EEG reports.•Being free and open-source software, EEG report parser can be freely used, improved and redistr...

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Bibliographic Details
Published in:Computer methods and programs in biomedicine 2023-12, Vol.242, p.107836-107836, Article 107836
Main Authors: Marinelli, Lucio, Cabona, Corrado, Pappalardo, Irene, Bellini, Anna, Ferrari, Alessandra, Micalizzi, Elisa, Audenino, Daniela, Villani, Flavio
Format: Article
Language:English
Subjects:
FLOSS
FOSS
Free/libre and open source software
MEDFLOSS
MHTML
PHP
PRESTIGE
Waveform
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Summary:•EEG features classification is useful for clinical and research purposes but can be time-consuming.•EEG report parser is a customizable script to quickly insert and extract feature tags from EEG reports.•Being free and open-source software, EEG report parser can be freely used, improved and redistributed. assess the effectiveness of a new method for classifying EEG recording features through the use of tags within reports. We present feature prevalence in a sample of patients with toxic-metabolic encephalopathy and discuss the advantages of this approach over existing classification systems. during EEG report creation, tags reflecting background activity, epileptiform features and periodic discharges were selected according to the findings of each recording. Reports including the tags have been collected and processed by the EEG report parser script written in PHP language. The resulting spreadsheet was analysed to calculate the prevalence and type of EEG features in a sample group of patients with toxic-metabolic encephalopathy. tag checking and extraction were very little time-consuming processes. Considering 5784 EEG recordings performed either in inpatients or outpatients over 2 years, toxic-metabolic aetiology was tagged in 218 (3.8 %). The most frequent background feature was severe slowing (5–6 Hz frequency), occurring in 79 (36.2 %). Epileptiform abnormalities were rare, reaching a maximum of 10 (4.6 %). Triphasic waves were tagged in 43 (19.7 %) recordings. tagging and parsing processes are very fast and integrated into the daily routine. Sample analysis in patients with toxic-metabolic encephalopathies showed EEG slowing as the prevalent feature, while triphasic waves occurred in a minority of recordings. Existing software such as “SCORE” (Holberg EEG) requires the replacement of the currently used software for EEG reporting, minimizing additional costs and training. EEG Report Parser is free and open-source software, so it can be freely adopted, modified and redistributed, allowing further improvement and adaptability.
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2023.107836