Abstract
Gait analysis provides a very large data volume coming from kinematic, kinetic, electromyographic (EMG) registers and physical examinations. The analysis and treatment of these data is difficult and time consuming. This work applies and explores exhaustively different analysis methods from data mining on these gait data. This study aims to provide a classification system based in gait patterns obtained from EMG records in children with spastic hemiplegia. The methods studied from data mining specifically for the classification task include SVM, neural networks, decision trees, regression logistic models and others. Different techniques of feature extraction and selection have been also employed and combined with classifications methods. The LMT algorithm provides the best result with 97% of instances classified correctly taking into account the indicators for 2 legs. A qualitative and quantitative validation were performed on the data.
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References
Farlex: The Free Dictionary by Farlex, http://medical-dictionary.thefreedictionary.com/gait
Loose, T., Mikut, R., Malberg, H., Simon, J., Schablowski, M., Rupp, R., Döderlein, L.: A Computer Based Method to Assess Gait Data. In: 2nd European Medical and Biological Engineering Conference, EMBEC (2002)
Simon, S.: Quantification of human motion: gait analysis—benefits and limitations to its application to clinical problems. J. of Biomechanics 37(12), 1869–1880 (2004)
Chau, T.: A review of analytical techniques for gait data. Part 1: fuzzy, statistical and fractal methods. Gait & Posture 13(1), 49–66 (2001)
Xu, S., Zhou, X., Sun, Y.: A novel gait analysis system based on adaptive neuro-fuzzy inference system. J. Expert Systems with Applications 37(2), 1265–1269 (2010)
Kamruzzaman, J., Begg, R.K.: Support Vector Machines and Other Pattern Recognition Approaches to the Diagnosis of Cerebral Palsy Gait. IEEE Transactions on Biomedical Engineering 53(12), 2479–2490 (2006)
Patikas, D., Wolf, S., Schuster, W., Armbrust, P., Dreher, T., Döderlein, L.: Electromyographic patterns in children with cerebral palsy: Do they change after surgery? Gait & Posture 26, 362–371 (2007)
Yang, M.-J., Zheng, H.-R., Wang, H.-Y., Mcclean, S., Harris, N.: Combining feature ranking with PCA: An application to gait analysis. In: International Conference on Machine Learning and Cybernetics (ICMLC), pp. 494–499 (2010)
Jianning, W.: Kernel-Based Feature Extraction for Automated Gait Classification Using Kinetics Data. In: Fourth International Conference on Natural Computation, ICNC 2008, vol. 4, pp. 162–166 (2008)
Zhang, B., Zhang, Y., Begg, R.: Gait classification in children with cerebral palsy by Bayesian approach. J. Pattern Recognition 42(4), 581–586 (2009)
Wolf, S., Loose, T., Schablowski, M., Döderlein, L., Rupp, R., Jürgen, H., Bretthauer, G., Mikut, R.: Automated feature assessment in instrumented gait analysis. Gait & Posture 23(3), 331–338 (2006)
Winters, T.F., Gage, J.R., Hicks, R.: Gait patterns in spastic hemiplegia in children and young adults. J. Bone Joint Surg (Am) 69, 437–441 (1987)
Fundación Hospital Ortopédico Infantil. Caracas Venezuela (2006), http://www.ortopedicoinfantil.org
Robb, J.: Gage, J.R. (ed.): The Treatment of Gait Problems in Cerebral Palsy. Clinics in Developmental Medicine No. 164–165. Mac Keith Press, London (2004); Gait & Posture 24(1), 130–130
Perry, J.: Distal Rectus Femoris Transfer. Developmental Medicine and Child Neurology 29, 153–158 (1987)
Viloria, N.: Electromyographic Evaluation of kinematics classification in Spastic Hemiplegic Patients with pathological gait. Msc Thesis in Biomedical Engineering, Simón Bolívar University, Caracas, Venezuela (2003)
Landwehr, N., Hall, M., Frank, E.: Logistic Model Trees (2003), http://www.cs.waikato.ac.nz/~ml/publications/2003/landwehr-etal.pdf
Gama, J.: Functional Trees. Machine Learning 55(3) (2004)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001), http://www.cs.colorado.edu/~grudic/teaching/CSCI5622_2004/RandomForests_ML_Journal.pdf
Holmes, G., Pfahringer, B., Kirkby, R., Frank, E., Hall, M.: Multiclass alternating decision trees. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 161–172. Springer, Heidelberg (2002)
Shi, H.: Best-first decision tree learning. Hamilton, NZ (2007)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth International Group, Belmont (1984)
Quinlan, R.J.: C4.5: Programs for machine learning. Morgan Kaufmann (1993)
Webb, G.: Decision Tree Grafting From the All-Tests-But-One Partition. In: Sixteenth Int. Joint Conf. on Artificial Intelligence, pp. 702–707. Morgan Kaufmann, San Francisco (1999)
Oliver, J.J., Hand, D.: Averaging Over Decision Stumps. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 231–241. Springer, Heidelberg (1994)
Bermejo, B.: Epidemiología Clínica aplicada a la toma de decisiones en medicina (2001), http://www.cfnavarra.es/salud/docencia.investigacion/textos/Monograf_1/Epidemiologia_clinica.pdf
Bravo, R.J., De Castro, O.C., Salazar, A.J.: Spastic hemiplegia gait characterization using support vector machines: Contralateral lower limb. Rev. Fac. Ing. UCV 21(2), 111–119 (2006)
Xu, G., Zhang, Y., Begg, R.: Mining Gait Pattern for Clinical Locomotion Diagnosis Based on Clustering Techniques. In: Li, X., Zaïane, O.R., Li, Z.-h. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 296–307. Springer, Heidelberg (2006)
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Aguilera, A., Subero, A., Mata-Toledo, R. (2013). Application of Data Mining Techniques on EMG Registers of Hemiplegic Patients. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2013. Lecture Notes in Computer Science(), vol 7987. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39736-3_20
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DOI: https://doi.org/10.1007/978-3-642-39736-3_20
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