Discrimination of Parkinsonian tremor from essential tremor by voting between different EMG signal processing techniques

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Parkinson's disease (PD) and essential tremor (ET) are the two most common disorders that cause involuntary muscle shaking movements, or what is called "tremor". PD is a neurodegenerative disease caused by the loss of dopamine receptors which control and adjust the movement of the body. On the other hand, ET is a neurological movement disorder which also causes tremors and shaking, but it is not related to dopamine receptor loss; it is simply a tremor. The differential diagnosis between these two disorders is sometimes difficult to make clinically because of the similarities of their symptoms; additionally, the available tests are complex and expensive. Thus, the objective of this paper is to discriminate between these two disorders with simpler, cheaper and easier ways by using electromyography (EMG) signal processing techniques. EMG and accelerometer records of 39 patients with PD and 41 with ET were acquired from the Hospital of Kiel University in Germany and divided into a trial group and a test group. Three main techniques were applied: the wavelet-based soft-decision technique, statistical signal characterization (SSC) of the spectrum of the signal, and SSC of the amplitude variation of the Hilbert transform. The first technique resulted in a discrimination efficiency of 80% on the trial set and 85% on the test set. The second technique resulted in an efficiency of 90% on the trial set and 82.5% on the test set. The third technique resulted in an 87.5% efficiency on the trial set and 65.5% efficiency on the test set. Lastly, a final vote was done to finalize the discrimination using these three techniques, and as a result of the vote, accuracies of 92.5%, 85.0% and 88.75% were obtained on the trial data, test data and total data, respectively.

Original languageEnglish
Pages (from-to)11-22
Number of pages12
JournalJournal of Engineering Research
Issue number1
Publication statusPublished - 2014


  • Discrimination efficiency
  • Essential tremor
  • Hilbert transform
  • Parkinson tremor
  • Statistical signal characterization
  • Wavelet-decomposition

ASJC Scopus subject areas

  • Engineering(all)


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