Title :
Relationship between pain and intersegmental spinal motion characteristics in low-back pain subjects
Author :
Dickey, J.P. ; Pierrynowski, M.R. ; Galea, V. ; Bednar, D.A. ; Yang, Simon X.
Author_Institution :
Sch. of Eng., Guelph Univ., Ont., Canada
Abstract :
This study was undertaken to determine the relationship between low-back pain and spinal motion. Percutaneous intra-pedicle screws were placed into the right and left L4 (or L5) and S1 segments of nine chronic low-back pain patients. Each of the subjects performed a standard battery of spinal motions including bending in all planes. At the completion of each desired motion, each subject was asked to self-report the pain they experienced on a 10 point scale. The 3D location of markers attached to the pedicle screws was recorded for each motion. Intra- and inter-vertebral motions were calculated. The time series data was reduced to the ranges of motion for each of the tests. A three-layer neural network with fast back-propagation learning algorithm was designed to investigate the relationship between the pain and the motion parameters. This model provided an accurate model for prediction of low-back pain from segmental spinal motion and also offered insights into the mechanisms causing mechanical low-back pain
Keywords :
backpropagation; biomechanics; feedforward neural nets; medical computing; neurophysiology; orthopaedics; time series; 3D location; bending; fast back-propagation learning algorithm; inter-vertebral motions; intersegmental spinal motion characteristics; low-back pain subjects; mechanical low-back pain; motion parameters; percutaneous intra-pedicle screw; three-layer neural network; time series data; Algorithm design and analysis; Battery charge measurement; Biology; Fasteners; Humans; Motion measurement; Neural networks; Pain; Predictive models; Testing;
Conference_Titel :
Systems, Man, and Cybernetics, 2000 IEEE International Conference on
Conference_Location :
Nashville, TN
Print_ISBN :
0-7803-6583-6
DOI :
10.1109/ICSMC.2000.884999