Artificial intelligence analysis of paraspinal power spectra

C W Oliver, W J Atsma

Research output: Contribution to journalArticlepeer-review

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 degrees 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.

Original languageEnglish
Pages (from-to)422-424
Number of pages3
JournalClinical Biomechanics
Volume11
Issue number7
Publication statusPublished - Oct 1996
Externally publishedYes

Fingerprint

Dive into the research topics of 'Artificial intelligence analysis of paraspinal power spectra'. Together they form a unique fingerprint.

Cite this