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

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

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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.

    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",
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    doi = "10.3390/s19194301",
    language = "English",
    volume = "19",
    journal = "Sensors (Switserland)",
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    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

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    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

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    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

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    KW - EWlectromyography

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    KW - Movement disorders

    KW - Parkinson’s disease

    KW - Tremor

    KW - Tremor stability index

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