Citation
Duhamel, P. (2002). Physiological assessment of pain (Doctoral dissertation, University of Nevada, Reno, Program in Cognitive and Brain Sciences).
Project context
This doctoral research investigated whether cortical evoked potentials could serve as objective indicators of the presence and severity of musculoskeletal pain.
The work combined experimental pain induction with electrophysiological recording to evaluate neurophysiological responses under symptomatic and asymptomatic conditions.
PerceptMX technology role
The project involved the design and engineering of an automated electrophysiological assessment system capable of stimulus control, signal acquisition, and real-time processing.
Custom algorithms were developed for component detection, peak extraction, amplitude and latency quantification, and automated classification of evoked potential features.
Methodological contribution
A repeated-measures experimental design examined cortical responses across baseline and post-exercise time points during delayed onset muscle soreness.
Advanced signal processing methods, including wavelet-based transformations, were implemented to enhance component identification stability and reduce subjectivity in peak detection.
Electrophysiological waveform components were quantified and analyzed in relation to concurrent self-reported pain intensity ratings to evaluate graded response patterns.
Outcome or impact
Amplitude characteristics of specific evoked potential components varied systematically with self-reported pain intensity, supporting a graded neurophysiological correlate of pain severity.
Electrophysiological features, including relative N4 amplitude measures, differentiated symptomatic from asymptomatic muscle during peak soreness intervals.
The findings supported the feasibility of automated cortical evoked potential analysis as a quantitative approach to pain presence detection and severity modeling.