Automatic characterization of the Parkinson disease by classifying the ipsilateral coordination and spatiotemporal gait patterns

Fernanda Sarmiento; Fabio Martínez; Eduardo Romero

doi:10.1117/12.2073529

28 January 2015 Automatic characterization of the Parkinson disease by classifying the ipsilateral coordination and spatiotemporal gait patterns

Fernanda Sarmiento, Fabio Martínez, Eduardo Romero

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Proceedings Volume 9287, 10th International Symposium on Medical Information Processing and Analysis; 928719 (2015) https://doi.org/10.1117/12.2073529
Event: Tenth International Symposium on Medical Information Processing and Analysis, 2014, Cartagena de Indias, Colombia

Abstract

Traditionally, the Parkinson disease is diagnosed and followed up by conventional clinical tests that are fully dependent on the expert experience. The diffuse boundary between normal and early Parkinson stages and the high variability of gait patterns difficult any objective characterization of this disease. An automatic characterization of the disease is herein proposed by mixing up different measures of the ipsilateral coordination and spatiotemporal gait patterns which are then classified with a classical support vector machine. The strategy was evaluated in a population with Parkinson and healthy control subjects, obtaining an average accuracy of 87% for the task of classification.

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Fernanda Sarmiento, Fabio Martínez, and Eduardo Romero "Automatic characterization of the Parkinson disease by classifying the ipsilateral coordination and spatiotemporal gait patterns", Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 928719 (28 January 2015); https://doi.org/10.1117/12.2073529

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