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

Journal Article (2019)
Author(s)

Frauke Luft (University of Twente)

Sarvi Sharifi (Amsterdam UMC)

Winfred Mugge (TU Delft - Biomechatronics & Human-Machine Control)

Alfred Schouten (University of Twente, TU Delft - Biomechatronics & Human-Machine Control)

Lo J. Bour (Amsterdam UMC)

A. F. Van Rootselaar (Amsterdam UMC)

Peter H. Veltink (University of Twente)

Tijtske Heida (University of Twente)

Research Group
Biomechatronics & Human-Machine Control
Copyright
© 2019 Frauke Luft, Sarvi Sharifi, W. Mugge, A.C. Schouten, Lo J. Bour, Anne Fleur van Rootselaar, Peter H. Veltink, Tijtske Heida
DOI related publication
https://doi.org/10.3390/s19194301
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 Frauke Luft, Sarvi Sharifi, W. Mugge, A.C. Schouten, Lo J. Bour, Anne Fleur van Rootselaar, Peter H. Veltink, Tijtske Heida
Research Group
Biomechatronics & Human-Machine Control
Issue number
19
Volume number
19
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Abstract

There is no objective gold standard to detect tremors. This concerns not only the choice of the algorithm and sensors, but methods are often designed to detect tremors in one specific group of patients during the performance of a specific task. Therefore, the aim of this study is twofold. First, an objective quantitative method to detect tremor windows (TWs) in accelerometer and electromyography recordings is introduced. Second, the tremor stability index (TSI) is determined to indicate the advantage of detecting TWs prior to analysis. Ten Parkinson's disease (PD) patients, ten essential tremor (ET) patients, and ten healthy controls (HC) performed a resting, postural and movement task. Data was split into 3-s windows, and the power spectral density was calculated for each window. The relative power around the peak frequency with respect to the power in the tremor band was used to classify the windows as either tremor or non-tremor. The method yielded a specificity of 96.45%, sensitivity of 84.84%, and accuracy of 90.80% of tremor detection. During tremors, significant differences were found between groups in all three parameters. The results suggest that the introduced method could be used to determine under which conditions and to which extent undiagnosed patients exhibit tremors.