Probabilistic model for individual assessment of central hyperexcitability using the nociceptive withdrawal reflex: a biomarker for chronic low back and neck pain
1 Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7, Aalborg, Øst 9220, Denmark
2 Department of Anesthesiology and Pain Medicine, University of Washington, 1959 NE Pacific Street, BB-1469, Box 356540, Seattle, WA 98195-6540, USA
3 Computer Vision and Media Technology Laboratory, Department of Architecture, Design and Media Technology, Aalborg University, Niels Jernes Vej 14, Aalborg, Øst 9220, Denmark
Citation and License
BMC Neuroscience 2013, 14:110 doi:10.1186/1471-2202-14-110Published: 3 October 2013
The nociceptive withdrawal reflex (NWR) has been proven to be a valuable tool in the objective assessment of central hyperexcitability in the nociceptive system at spinal level that is present in some chronic pain disorders, particularly chronic low back and neck pain. However, most of the studies on objective assessment of central hyperexcitability focus on population differences between patients and healthy individuals and do not provide tools for individual assessment. In this study, a prediction model was developed to objectively assess central hyperexcitability in individuals. The method is based on statistical properties of the EMG signals associated with the nociceptive withdrawal reflex. The model also supports individualized assessment of patients, including an estimation of the confidence of the predicted result.
up to 80% classification rates were achieved when differentiating between healthy volunteers and chronic low back and neck pain patients. EMG signals recorded after stimulation of the anterolateral and heel regions and of the sole of the foot presented the best prediction rates.
A prediction model was proposed and successfully tested as a new approach for objective assessment of central hyperexcitability in the nociceptive system, based on statistical properties of EMG signals recorded after eliciting the NWR. Therefore, the present statistical prediction model constitutes a first step towards potential applications in clinical practice.