A decision tree classifier for postural and movement conditions in Essential Tremor patients

P. Shukla*, I. Basu, D. Graupe, D. Tuninetti, K. V. Slavin, L. Verhagen Metman, D. M. Corcos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This paper proposes a decision tree based classifier to discriminate between movement and postural conditions in Essential Tremor (ET) patients when their Deep Brain Stimulator (DBS) is switched OFF and they do not yet present tremor symptoms. This aims to be the first stage of a fully automated closed-loop ON-OFF DBS system in which the algorithm for prediction of tremor onset uses optimized parameters depending on the patient's postural or movement condition. The classifier inputs are the power of the surface-electromyogram (sEMG) and accelerometer (Acc) signals recorded at the symptomatic extremities of the patients. The proposed classification tree uses Gini splitting rule and an optimized pruning scheme. The classifier achieves an overall accuracy of 96.55% by correctly classifying 112 out of 116 trials in four ET patients: 49 trials were in the movement condition and 67 were in postural condition. A classification accuracy of 100.00% (49 trials out of 49) and 94.03% (63 trials out of 67) is achieved for movement and posture conditions, respectively.

Original languageEnglish (US)
Title of host publication2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Pages117-120
Number of pages4
DOIs
StatePublished - 2013
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: Nov 6 2013Nov 8 2013

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Country/TerritoryUnited States
CitySan Diego, CA
Period11/6/1311/8/13

Keywords

  • Closed-loop deep brain stimulation
  • Decision tree classifier
  • Essential Tremor
  • Gini index impurity function
  • Surface EMG

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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