Abstract
Gait analysis is used for non-automated and automated diagnosis of various neuromuskuloskeletal abnormalities. Automated systems are important in assisting physicians for diagnosis of various diseases. This study presents preliminary steps of designing a clinical decision support system for semi-automated diagnosis of knee illnesses by using temporal gait data. This study compares the gait of 111 patients with 110 age-matched normal subjects. Different feature reduction techniques, (FFT, averaging and PCA) are compared by the Mahalanobis Distance criterion and by performances of well known classifiers. The feature selection criteria used reveals that the gait measurements for different parts of the body such as knee or hip to be more effective for detection of the illnesses. Then, a set of classifiers is tested by a ten-fold cross validation approach on all datasets. It is observed that average based datasets performed better than FFT applied ones for almost all classifiers while PCA applied dataset performed better for linear classifiers. In general, nonlinear classifiers performed quite well (best error rate is about 0.035) and better than the linear ones.
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References
Simon, S.R.: Quantification of human motion: gait analysis—benefits and limitations to its application to clinical problems. Journal of Biomechanics 37, 1869–1880 (2004)
Chau, T.: A review of analytical techniques for gait data, Part 2: Neural Networks and Wavelet Methods. Gait Posture 13, 102–120 (2001)
Kohle, M., Merkl, D., Kastner, J.: Clinical gait analysis by neural networks: issues and experiences. In: Proceedings of the 10th IEEE Symposium on Computer-Based Medical Systems, p. 138 (1997)
Barton, J.G., Lees, A.: An application of neural networks for distinguishing gait patterns on the basis of hip-knee joint angle diagrams. Gait & Posture 5, 28–33 (1997)
Sen Koktas, N., Yalabik, N., Yavuzer, G.: An Intelligent Clinical Decision Support System for Analyzing Neuromuskuloskeletal Disorders. In: International Workshop on Pattern Recognition in Information Systems, pp. 29–37 (2008)
Sen Koktas, N., Yalabik, N., Yavuzer, G.: Ensemble Classifiers for Medical Diagnosis of Knee Osteoarthritis Using Gait Data. In: Proceeding of IEEE International Conference on Machine Learning and Applications (2006)
Begg, R., Kamruzzaman, J.: A Comparison of Neural Networks and Support Vector Machines for Recognizing Young-Old Gait Patterns. In: Proceeding of IEEE TENCON Conference (2003)
Begg, R., Palaniswami, M., Owen, B.: Support Vector Machines for Automated Gait Classification. IEEE Transactions on Biomedical Engineering 1, 52–65 (2005)
Salazar, A.J., De Castro, O.C., Bravo, R.J.: Novel approach for spastic hemiplegia classification through the use of support vector machines. In: Proceedings of the 26th Annual International Conference of the Engineering in Medicine and Biology Society (2004)
Dobson, F., Morris, M.E., Baker, R., Graham, H.K.: Gait classification in children with cerebral palsy: A systematic review. Gait and Posture 25, 140–152 (2007)
Chau, T.: A review of analytical techniques for gait data, Part 1: Fuzzy, statistical and fractal methods. Gait Posture 13, 49–66 (2001)
Deluzio, K.J., Astephen, J.L.: Biomechanical features of gait waveform data associated with knee Osteoarthritis: An application of principal component analysis. Gait and Posture 25, 86–93 (2007)
Gök, H., Ergin, S., Yavuzer, G.: Kinetic and kinematic characteristics of gait in patients with medial knee arthrosis. Acta Orthop Scand 2002 73(6), 647–652 (2002)
Kaufman, K., Hughes, C., Morrey, B., An, K.: Gait characteristics of patients with knee Osteoarthritis. Journal of Biomechanics 34, 907–915 (2001)
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons, New York (2001)
Duin, R.P.W.: PRTOOLS (version 4). A Matlab toolbox for pattern recognition. Pattern Recognition Group, Delft University of Technology (February 2004)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, Hoboken (2004)
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Şen Köktaş, N., Duin, R.P.W. (2010). Statistical Analysis of Gait Data to Assist Clinical Decision Making. In: Caputo, B., Müller, H., Syeda-Mahmood, T., Duncan, J.S., Wang, F., Kalpathy-Cramer, J. (eds) Medical Content-Based Retrieval for Clinical Decision Support. MCBR-CDS 2009. Lecture Notes in Computer Science, vol 5853. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11769-5_6
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DOI: https://doi.org/10.1007/978-3-642-11769-5_6
Publisher Name: Springer, Berlin, Heidelberg
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