Chronic pain impacts more than one in five adults in the United States (US) and the costs associated with the condition amount to hundreds of billions of dollars annually. Despite the tremendous impact of chronic pain globally, the standard of care for diagnosis depends on subjective self-reporting of pain state, with no objective assessment procedure available. This study investigated the application of signal processing and machine learning to electroencephalography (EEG) data for the development of classification algorithms capable of differentiating subjects with diverse chronic pain etiologies from pain-free subjects. The study population included participants experiencing various types of chronic pain, including nociceptive, neuropathic, and mixed etiological pain conditions. Chronic pain diagnoses were based on clinical evaluation by participating physicians and adhered to the International Association for the Study of Pain (IASP) definition, requiring pain persistence for >3 months and associated functional impairment or emotional distress. Data from 186 participants were used for algorithm development, including 35 healthy controls and 151 chronic pain patients. Machine learning methodologies were applied to the data, with Elastic Net chosen as the optimal methodology.. The classifier was able to differentiate pain versus no pain subjects with an accuracy of 79.6%, sensitivity of 82.2%, and specificity of 66.7%. This study incorporates the multidimensional nature of chronic pain, ensuring that our methods and interpretations align with current clinical and research standards. This study represents a step toward integrating EEG-based biomarkers into clinical workflows for chronic pain assessment, bridging the gap between subjective reporting and objective diagnostic tools.

Background: There is an urgent need for objective criteria adjunctive to standard clinical assessment of acute Traumatic Brain Injury (TBI). Details of the development of a quantitative index to identify structural brain injury based on brain electrical activity will be described.Methods: Acute closed head injured and normal patients (n=1470) were recruited from 16 US Emergency Departments and evaluated using brain electrical activity (EEG) recorded from forehead electrodes. Patients had high GCS (median=15), and most presented with low suspicion of brain injury. Patients were divided into a CT positive (CT+) group and a group with CT negative findings or where CT scans were not ordered according to standard assessment (CT-/CT_NR). Three different classifier methodologies, Ensemble Harmony, Least Absolute Shrinkage and Selection Operator (LASSO), and Genetic Algorithm (GA), were utilized.Results: Similar performance accuracy was obtained for all three methodologies with an average sensitivity/specificity of 97.5%/59.5%, area under the curves (AUC) of 0.90 and average Negative Predictive Validity (NPV) > 99%. Sensitivity was highest for CT+ cases with potentially life threatening hematomas, where two of three classifiers were 100%.Conclusion: Similar performance of these classifiers suggests that the optimal separation of the populations was obtained given the overlap of the underlying distributions of features of brain activity. High sensitivity to CT+ injuries (highest in hematomas) and specificity significantly higher than that obtained using ED guidelines for imaging, supports the enhanced clinical utility of this technology and suggests the potential role in the objective, rapid and more optimal triage of TB! patients. Published by Elsevier Ltd.