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帕金森远程监控数据集

帕金森远程监控数据集

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Medical Regression

Data Set Information:该数据集由一系列生物医学语音测量数据组成,这些数据来自42名早期帕金森病患者,他们被招募参加为期6个月......

数据结构 ? 261K

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Data Set Information:

    该数据集由一系列生物医学语音测量数据组成,这些数据来自42名早期帕金森病患者,他们被招募参加为期6个月的远程症状进展监测远程监测设备试验。这些录音是在患者家中自动拍摄的。

    表中的列包含受试者编号、受试者年龄、受试者性别、从基线招募日期开始的时间间隔、运动UPDR、总UPDR和16项生物医学语音测量。每行对应于这些人5875个语音记录中的一个。数据的主要目的是预测16个语音测量的运动和UPDRS总分(“运动和UPDRS”和“总UPDRS”)。


    The data is in ASCII CSV format. The rows of the CSV file contain an instance corresponding to one voice recording. There are around 200 recordings per patient, the subject number of the patient is identified in the first column. For further information or to pass on comments, please contact Athanasios Tsanas (tsanasthanasis '@' gmail.com) or Max Little (littlem '@' physics.ox.ac.uk).

    Further details are contained in the following reference -- if you use this dataset, please cite:
    Athanasios Tsanas, Max A. Little, Patrick E. McSharry, Lorraine O. Ramig (2009),
    'Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests',
    IEEE Transactions on Biomedical Engineering (to appear).

    Further details about the biomedical voice measures can be found in:
    Max A. Little, Patrick E. McSharry, Eric J. Hunter, Lorraine O. Ramig (2009),
    'Suitability of dysphonia measurements for telemonitoring of Parkinson's disease',
    IEEE Transactions on Biomedical Engineering, 56(4):1015-1022


    Attribute Information:

    subject# - Integer that uniquely identifies each subject
    age - Subject age
    sex - Subject gender '0' - male, '1' - female
    test_time - Time since recruitment into the trial. The integer part is the number of days since recruitment.
    motor_UPDRS - Clinician's motor UPDRS score, linearly interpolated
    total_UPDRS - Clinician's total UPDRS score, linearly interpolated
    Jitter(%),Jitter(Abs),Jitter:RAP,Jitter:PPQ5,Jitter:DDP - Several measures of variation in fundamental frequency
    Shimmer,Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,Shimmer:APQ11,Shimmer:DDA - Several measures of variation in amplitude
    NHR,HNR - Two measures of ratio of noise to tonal components in the voice
    RPDE - A nonlinear dynamical complexity measure
    DFA - Signal fractal scaling exponent
    PPE - A nonlinear measure of fundamental frequency variation


    Relevant Papers:

    Little MA, McSharry PE, Hunter EJ, Ramig LO (2009),
    'Suitability of dysphonia measurements for telemonitoring of Parkinson's disease',
    IEEE Transactions on Biomedical Engineering, 56(4):1015-1022

    Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM.
    'Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection',
    BioMedical Engineering onLine 2007, 6:23 (26 June 2007)


    Citation Request:

    If you use this dataset, please cite the following paper:
    A Tsanas, MA Little, PE McSharry, LO Ramig (2009)
    'Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests',
    IEEE Transactions on Biomedical Engineering (to appear).


    The dataset was created by Athanasios Tsanas (tsanasthanasis '@' gmail.com) and Max Little (littlem '@' physics.ox.ac.uk) of the University of Oxford, in collaboration with 10 medical centers in the US and Intel Corporation who developed the telemonitoring device to record the speech signals. The original study used a range of linear and nonlinear regression methods to predict the clinician's Parkinson's disease symptom score on the UPDRS scale.

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