Abstract
Evaluation of the locomotor function is important for several clinical applications (e.g. fall risk of the elderly, characterization of a disease with motor complications). We consider the Timed Up and Go test which is widely used to evaluate the locomotor function in Parkinson’s Disease (PD). Twenty PD and twenty age-matched control subjects performed an instrumented version of the test, where wearable accelerometers were used to gather quantitative information. Several measures were extracted from the acceleration signals; the aim is to find, by means of a feature selection, the best set that can discriminate between healthy and PD subjects. A wrapper feature selection was implemented with an exhaustive search for subsets from 1 to 3 features. A nested leave-one-out cross validation (LOOCV) was implemented, to limit a possible selection bias. With the selected features a good accuracy is obtained (7.5% of misclassification rate) in the classification between PD and healthy subjects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Weiss, A., Herman, T., Plotnik, M., Brozgol, M., et al.: Can an accelerometer enhance the utility of the Timed Up & Go Test when evaluating patients with Parkinson’s disease? Med. Eng. & Phys. 32(2), 119–125 (2010)
Zampieri, C., Salarian, A., Carlson-Kuhta, P., Aminian, K., et al.: The instrumented timed up and go test: potential outcome measure for disease modifying therapies in Parkinson’s disease. J. of Neurol., Neurosurg. & Psychiatry 81(2), 171–176 (2009)
Brewer, B.R., Pradhan, S., Carvell, G., Delitto, A.: Feature selection for classification based on fine motor signs of Parkinson’s disease. In: 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2009)
Plotnik, M., Giladi, N., Hausdorff, J.M.: A new measure for quantifying the bilateral coordination of human gait: effects of aging and Parkinson’s disease. Exp. Brain Res. 181(4), 561–570 (2007)
Simon, R., Radmacher, M.D., Dobbin, K., McShane, L.M.: J. Natl. Cancer Inst. 95(1), 14–18 (2003)
Kohavi, R., John, G.H.: Wrappers for Feature Subset Selection. Art. Intel. 97(1-2), 273–324 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Palmerini, L., Rocchi, L., Mellone, S., Valzania, F., Chiari, L. (2013). A Clinical Application of Feature Selection: Quantitative Evaluation of the Locomotor Function. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2010. Communications in Computer and Information Science, vol 272. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29764-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-29764-9_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29763-2
Online ISBN: 978-3-642-29764-9
eBook Packages: Computer ScienceComputer Science (R0)