Discrimination of Parkinsonian tremor from essential tremor using statistical signal characterization of the spectrum of accelerometer signal

A. Hossen, M. Muthuraman, Z. Al-Hakim, J. Raethjen, G. Deuschl, U. Heute

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

A new technique for discrimination of Parkinson tremor from essential tremor is presented in this paper. This technique is based on Statistical Signal Characterization (SSC) of the spectrum of the accelerometer signal. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. Two sets of data are used. The training set, which consists of 21 essential-tremor (ET) subjects and 19 Parkinson-disease (PD) subjects, is used to obtain the threshold value of the classification factor differentiating between the two subjects. The test data set, which consists of 20 ET and 20 PD subjects, is used to test the technique and evaluate its performance. Three of twelve newly derived SSC parameters show good discrimination results. Specific results of those three parameters on training data and test data are shown in detail. A linear combination of the effects of those parameters on the discrimination results is also included. A total discrimination accuracy of 90% is obtained.

Original languageEnglish
Pages (from-to)513-531
Number of pages19
JournalBio-Medical Materials and Engineering
Volume23
Issue number6
DOIs
Publication statusPublished - 2013

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Accelerometers
Neurology

Keywords

  • accelerometer signals
  • discrimination
  • essential tremor
  • FFT spectrum
  • Parkinson tremor
  • Statistical signal characterization

ASJC Scopus subject areas

  • Biomedical Engineering
  • Biomaterials

Cite this

Discrimination of Parkinsonian tremor from essential tremor using statistical signal characterization of the spectrum of accelerometer signal. / Hossen, A.; Muthuraman, M.; Al-Hakim, Z.; Raethjen, J.; Deuschl, G.; Heute, U.

In: Bio-Medical Materials and Engineering, Vol. 23, No. 6, 2013, p. 513-531.

Research output: Contribution to journalArticle

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