Analysis of quantitative electroencephalography (qEEG) patterns in fibromyalgia patients as compared to a normative database

This dissertation seeks to understand whether digital analysis of electroencephalography (EEG), often referred to as quantitative electroencephalography (qEEG), when compared to a lifespan normative database, can lead to identifiable EEG patterns consistent with fibromyalgia (FM), a chronic pain condition. This study is a diagnostic analysis trial involving human participants in a traditional research office setting. Participants for the study were volunteers who had their EEG measured under a controlled condition. There were a total of 19 participants who completed this study between the ages of 24-65 years of age. Participation requirements included English speaking, minimum of an 8th grade education, diagnosed FM by a physician using ACR 1990 criteria for at least 24 months, no pre-existing medical conditions (except anxiety and depression), no pregnancy and no litigation. The results presented support that qEEG may be a reliable measurement tool that can be useful in identifying EEG patterns of FM patients, which could lead to improved diagnosis and treatment. The null hypothesis that the EEG (qEEG) activity of FM patients will not exhibit abnormal patterns as compared to health normal controls (HNCs) was rejected at the P = 0.001 alpha level, affirming the alternative hypothesis that the EEG (qEEG) activity is distinctly identifiable in FM patients. Specifically, reduced coherence was identifiable in 100% of the FM patients within the anterior regions of the brain.