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

Lucio Marinelli¹, Corrado Cabona², Irene Pappalardo²

¹Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Italy; IRCCS Ospedale Policlinico San Martino, Department of Neuroscience, Division of Clinical Neurophysiology and Epilepsy Centre, Genova, Italy.

Computer Methods and Programs in Biomedicine

|October 5, 2023

Summary

This study introduces a fast, tag-based method for classifying electroencephalogram (EEG) features in reports. Toxic-metabolic encephalopathy patients showed prevalent EEG slowing, with triphasic waves occurring less frequently.

Keywords:

FLOSS FOSS Free/libre and open source software MEDFLOSS MHTML PHP PRESTIGE Waveform

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Area of Science:

Neurology
Medical Informatics

Background:

Existing electroencephalogram (EEG) classification systems can be cumbersome.
A novel, tag-based approach aims to streamline EEG report analysis.

Purpose of the Study:

To assess the effectiveness of a new tag-based method for classifying EEG recording features.
To determine the prevalence of specific EEG features in patients with toxic-metabolic encephalopathy.

Main Methods:

EEG reports were tagged during creation with features like background activity, epileptiform discharges, and periodic discharges.
A PHP script parsed tagged reports, and a spreadsheet analysis calculated feature prevalence.
The study focused on patients with toxic-metabolic encephalopathy.

Main Results:

Tagging and parsing were time-efficient, integrated into daily workflows.
Out of 5784 EEG recordings, 218 (3.8%) were tagged for toxic-metabolic aetiology.
Severe slowing (5-6 Hz) was the most frequent background feature (36.2%), while epileptiform abnormalities were rare (4.6%). Triphasic waves were present in 19.7% of recordings.

Conclusions:

The tag-and-parse method is rapid and easily integrated into clinical practice.
EEG slowing is a prevalent feature in toxic-metabolic encephalopathies, unlike triphasic waves.
The free, open-source EEG Report Parser offers an adaptable alternative to existing software, minimizing costs and training.