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

Frauke Luft, Sarvi Sharifi, Winfred Mugge, Alfred C. Schouten, Lo J. Bour, Anne Fleur van Rootselaar, Peter H. Veltink, Tijtske Heida

Research output: Contribution to journalArticleAcademicpeer-review

18 Downloads (Pure)

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.

Original languageEnglish
JournalSensors (Basel, Switzerland)
Volume19
Issue number19
DOIs
Publication statusPublished - 4 Oct 2019

Fingerprint

tremors
Electromyography
Power spectral density
Movement Disorders
intermittency
Tremor
Accelerometers
disorders
Sensors
electromyography
Essential Tremor
Parkinson disease
accelerometers
Parkinson Disease
recording
Sensitivity and Specificity

Keywords

  • Accelerometers
  • Automatic detection
  • EWlectromyography
  • Essential tremor
  • Movement disorders
  • Parkinson’s disease
  • Tremor
  • Tremor stability index

Cite this

@article{f561941e4f2c48ff945984ad509242d2,
title = "A Power Spectral Density-Based Method to Detect Tremor and Tremor Intermittency in Movement Disorders",
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.",
keywords = "Accelerometers, Automatic detection, EWlectromyography, Essential tremor, Movement disorders, Parkinson’s disease, Tremor, Tremor stability index",
author = "Frauke Luft and Sarvi Sharifi and Winfred Mugge and Schouten, {Alfred C.} and Bour, {Lo J.} and {van Rootselaar}, {Anne Fleur} and Veltink, {Peter H.} and Tijtske Heida",
year = "2019",
month = "10",
day = "4",
doi = "10.3390/s19194301",
language = "English",
volume = "19",
journal = "Sensors (Switserland)",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute",
number = "19",

}

A Power Spectral Density-Based Method to Detect Tremor and Tremor Intermittency in Movement Disorders. / Luft, Frauke; Sharifi, Sarvi; Mugge, Winfred; Schouten, Alfred C.; Bour, Lo J.; van Rootselaar, Anne Fleur; Veltink, Peter H.; Heida, Tijtske.

In: Sensors (Basel, Switzerland), Vol. 19, No. 19, 04.10.2019.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - A Power Spectral Density-Based Method to Detect Tremor and Tremor Intermittency in Movement Disorders

AU - Luft, Frauke

AU - Sharifi, Sarvi

AU - Mugge, Winfred

AU - Schouten, Alfred C.

AU - Bour, Lo J.

AU - van Rootselaar, Anne Fleur

AU - Veltink, Peter H.

AU - Heida, Tijtske

PY - 2019/10/4

Y1 - 2019/10/4

N2 - 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.

AB - 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.

KW - Accelerometers

KW - Automatic detection

KW - EWlectromyography

KW - Essential tremor

KW - Movement disorders

KW - Parkinson’s disease

KW - Tremor

KW - Tremor stability index

UR - http://www.scopus.com/inward/record.url?scp=85072960146&partnerID=8YFLogxK

U2 - 10.3390/s19194301

DO - 10.3390/s19194301

M3 - Article

VL - 19

JO - Sensors (Switserland)

JF - Sensors (Switserland)

SN - 1424-8220

IS - 19

ER -