Classification of neck movement patterns related to whiplash-associated disorders using neural networks

• Published in: IEEE journal of biomedical and health informatics 2003-12, Vol.7 (4), p.412-418
• Main Authors: Grip, H., Ohberg, F., Wiklund, U., Sterner, Y., Karlsson, J.S., Gerdle, B.
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Artificial Intelligence; Backpropagation; Control systems; Data mining; Decision support system; Decision support systems; Female; Head Movements; Head rotation; Humans; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Injuries; Instantaneous helical axis; Male; Medicin och hälsovetenskap; Motion analysis; Neck; Neck - physiopathology; Neural network; Neural networks; Neural Networks (Computer); Pain; Pattern analysis; Pattern Recognition, Automated; Physical Examination - methods; Principal components analysis; Reproducibility of Results; Resilient backpropagation; Road accidents; Sensitivity and Specificity; Studies; Video Recording - methods; Whiplash Injuries - classification; Whiplash Injuries - diagnosis; Whiplash Injuries - physiopathology; Whiplash-associated disorders (WAD)
• ISSN: 1089-7771; 2168-2194; 1558-0032; 1558-0032; 2168-2208
• DOI: 10.1109/TITB.2003.821322

This paper presents a new method for classification of neck movement patterns related to whiplash-associated disorders (WAD) using a resilient backpropagation neural network (BPNN). WAD are a common diagnosis after neck trauma, typically caused by rear-end car accidents. Since physical injuries seldom are found with present imaging techniques, the diagnosis can be difficult to make. The active range of the neck is often visually inspected in patients with neck pain, but this is a subjective measure, and a more objective decision support system, that gives a reliable and more detailed analysis of neck movement pattern, is needed. The objective of this study was to evaluate the predictive ability of a BPNN, using neck movement variables as input. Three-dimensional (3-D) neck movement data from 59 subjects with WAD and 56 control subjects were collected with a ProReflex system. Rotation angle and angle velocity were calculated using the instantaneous helical axis method and motion variables were extracted. A principal component analysis was performed in order to reduce data and improve the BPNN performance. BPNNs with six hidden nodes had a predictivity of 0.89, a sensitivity of 0.90 and a specificity of 0.88, which are very promising results. This shows that neck movement analysis combined with a neural network could build the basis of a decision support system for classifying suspected WAD, even though further evaluation of the method is needed.