EEG background activity described by a large computerized database.

Area of Science:

• Neuroscience
• Medical Informatics

Background:

• Electroencephalography (EEG) is crucial for assessing brain activity.
• Routine EEG assessments require efficient classification and storage systems.
• Understanding changes in general background activity (GBA) and alpha rhythm (AR) with age is important.

Purpose of the Study:

• To demonstrate the application of novel software for classifying and storing routine EEG data.
• To evaluate general background activity (GBA) and alpha rhythm (AR) in a large cohort of prospective EEGs.
• To analyze age-related changes in GBA and AR using a large EEG database.

Main Methods:

• Utilized a computerized description system linked to an EEG database for 4651 consecutive patients.
• Visually assessed EEGs, describing AR and GBA (frequency and amplitude) separately.
• Analyzed trends across age groups, genders, and pathologies, comparing interpretations from four electroencephalographers.

Main Results:

• Alpha rhythm (AR) frequencies decreased and slowed with age starting at 45, independent of gender or non-AR pathology.
• Females exhibited higher AR frequencies than males.
• EEGs with non-GBA pathology showed lower GBA frequencies and higher amplitudes; higher GBA amplitudes correlated with lower GBA frequencies in normal EEGs across all ages.
• Interobserver agreement showed consistent trends but variations in precise frequency and amplitude assessments.

Conclusions:

• A categorized EEG database facilitates evaluation of interobserver variation and quality control.
• Statistical analysis of stored EEG data yields novel information on lifelong development of AR and GBA.
• The developed software effectively supports large-scale EEG data analysis and quality assessment.