Artificial intelligence analysis of paraspinal power spectra

Abstract

Objective. As an aid to discrimination of sufferers with back pain an artificial intelligence neural network was constructed to differentiate paraspinal power spectra. Design. Clinical investigation using surface electromyography. Method. The surface electromyogram power spectra from 60 subjects, 33 non-back-pain sufferers and 27 chronic back pain sufferers were used to construct a back propagation neural network that was then tested. Subjects were placed on a test frame in 30 ° of lumbar forward flexion. An isometric load of two-thirds maximum voluntary contraction was held constant for 30 s whilst surface electromyograms were recorded at the level of the L 4–5. Paraspinal power spectra were calculated and loaded into the input layer of a three-layer back propagation network. The neural network classified the spectra into normal or back pain type. Results. The back propagation neural was shown to have satisfactory convergence with a specificity of 79% and a sensitivity of 80%. Conclusions. Artificial intelligence neural networks appear to be a useful method of differentiating paraspinal power spectra in back-pain sufferers.