Abstract
Diagnosing different diseases with some similarities (e.g. in symptoms) using an automated approach and a simple and available feature may help physicians to focus on the correct disease and its treatment and to avoid wasting time on diagnosis, Especially for diseases that the treatment time is critical to control and cure. In this study, we try to develop a new automated approach for classifying (diagnosing) locomotive patients using features that may be extracted from their gait signal. We selected 3 groups of patients: patients with Huntington’s disease, Parkinson’s disease and Amyotrophic Lateral Sclerosis- and a group of healthy control subjects. Examining different available classifiers on all proposed features, we have introduced a novel feature with acceptably low error rate using quadratic Bayes classifier.
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Banaie, M., Mikaili, M., Pooyan, M. (2009). A Novel Automatic Method on Diagnosing Movement Disorders. In: Yu, W., Sanchez, E.N. (eds) Advances in Computational Intelligence. Advances in Intelligent and Soft Computing, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03156-4_38
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DOI: https://doi.org/10.1007/978-3-642-03156-4_38
Publisher Name: Springer, Berlin, Heidelberg
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