Literature DB >> 12780154

Tremor classification and tremor time series analysis.

Gunther Deuschl1, Michael Lauk, Jens Timmer.   

Abstract

The separation between physiologic tremor (PT) in normal subjects and the pathological tremors of essential tremor (ET) or Parkinson's disease (PD) was investigated on the basis of monoaxial accelerometric recordings of 35 s hand tremor epochs. Frequency and amplitude were insufficient to separate between these conditions, except for the trivial distinction between normal and pathologic tremors that is already defined on the basis of amplitude. We found that waveform analysis revealed highly significant differences between normal and pathologic tremors, and, more importantly, among different forms of pathologic tremors. We found in our group of 25 patients with PT and 15 with ET a reasonable distinction with the third momentum and the time reversal invariance. A nearly complete distinction between these two conditions on the basis of the asymmetric decay of the autocorrelation function. We conclude that time series analysis can probably be developed into a powerful tool for the objective analysis of tremors. (c) 1995 American Institute of Physics.

Entities:  

Year:  1995        PMID: 12780154     DOI: 10.1063/1.166084

Source DB:  PubMed          Journal:  Chaos        ISSN: 1054-1500            Impact factor:   3.642


  8 in total

1.  Long-term EMG recordings differentiate between parkinsonian and essential tremor.

Authors:  S Breit; S Spieker; J B Schulz; T Gasser
Journal:  J Neurol       Date:  2008-01-22       Impact factor: 4.849

2.  Identifying peer experts in online health forums.

Authors:  V G Vinod Vydiswaran; Manoj Reddy
Journal:  BMC Med Inform Decis Mak       Date:  2019-04-04       Impact factor: 2.796

3.  A Power Spectral Density-Based Method to Detect Tremor and Tremor Intermittency in Movement Disorders.

Authors:  Frauke Luft; Sarvi Sharifi; Winfred Mugge; Alfred C Schouten; Lo J Bour; Anne-Fleur van Rootselaar; Peter H Veltink; Tijtske Heida
Journal:  Sensors (Basel)       Date:  2019-10-04       Impact factor: 3.576

Review 4.  Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms.

Authors:  Anirudha S Chandrabhatla; I Jonathan Pomeraniec; Alexander Ksendzovsky
Journal:  NPJ Digit Med       Date:  2022-03-18

5.  Aging, neuromuscular decline, and the change in physiological and behavioral complexity of upper-limb movement dynamics.

Authors:  S Morrison; K M Newell
Journal:  J Aging Res       Date:  2012-08-01

6.  C9ORF72 intermediate repeat copies are a significant risk factor for Parkinson disease.

Authors:  Karen Nuytemans; Güney Bademci; Martin M Kohli; Gary W Beecham; Liyong Wang; Juan I Young; Fatta Nahab; Eden R Martin; John R Gilbert; Michael Benatar; Jonathan L Haines; William K Scott; Stephan Züchner; Margaret A Pericak-Vance; Jeffery M Vance
Journal:  Ann Hum Genet       Date:  2013-07-12       Impact factor: 1.670

7.  A Novel Posture for Better Differentiation Between Parkinson's Tremor and Essential Tremor.

Authors:  Bin Zhang; Feifei Huang; Jun Liu; Dingguo Zhang
Journal:  Front Neurosci       Date:  2018-05-17       Impact factor: 4.677

8.  Tremor analysis with wearable sensors correlates with outcome after thalamic deep brain stimulation.

Authors:  Dayle Rüegge; Sujitha Mahendran; Lennart H Stieglitz; Markus F Oertel; Roger Gassert; Olivier Lambercy; Christian R Baumann; Lukas L Imbach
Journal:  Clin Park Relat Disord       Date:  2020-08-05
  8 in total

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