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-oneout 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.

A Clinical Application of Feature Selection: Quantitative Evaluation of the Locomotor Function

PALMERINI, LUCA;ROCCHI, LAURA;MELLONE, SABATO;CHIARI, LORENZO
2013

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-oneout 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.
IC3K 2010: Communications in Computer and Information Science 272
151
157
L. Palmerini; L. Rocchi; S. Mellone; F. Valzania; L. Chiari
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/125139
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