107 lines
4.3 KiB
Text
Executable file
107 lines
4.3 KiB
Text
Executable file
Parkinsons Telemonitoring Data Set
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Abstract: Oxford Parkinson's Disease Telemonitoring Dataset
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Data Set Characteristics: Multivariate
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Attribute Characteristics: Integer, Real
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Associated Tasks: Regression
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Number of Instances: 5875
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Number of Attributes: 26
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Area: Life
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Date Donated: 2009-10-29
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SOURCE:
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The dataset was created by Athanasios Tsanas (tsanasthanasis '@' gmail.com)
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and Max Little (littlem '@' physics.ox.ac.uk) of the University of Oxford, in
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collaboration with 10 medical centers in the US and Intel Corporation who
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developed the telemonitoring device to record the speech signals. The
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original study used a range of linear and nonlinear regression methods to
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predict the clinician's Parkinson's disease symptom score on the UPDRS scale.
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DATA SET INFORMATION:
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This dataset is composed of a range of biomedical voice measurements from 42
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people with early-stage Parkinson's disease recruited to a six-month trial of
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a telemonitoring device for remote symptom progression monitoring. The
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recordings were automatically captured in the patient's homes.
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Columns in the table contain subject number, subject age, subject gender,
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time interval from baseline recruitment date, motor UPDRS, total UPDRS, and
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16 biomedical voice measures. Each row corresponds to one of 5,875 voice
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recording from these individuals. The main aim of the data is to predict the
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motor and total UPDRS scores ('motor_UPDRS' and 'total_UPDRS') from the 16
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voice measures.
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The data is in ASCII CSV format. The rows of the CSV file contain an instance
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corresponding to one voice recording. There are around 200 recordings per
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patient, the subject number of the patient is identified in the first column.
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For further information or to pass on comments, please contact Athanasios
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Tsanas (tsanasthanasis '@' gmail.com) or Max Little (littlem '@'
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physics.ox.ac.uk).
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Further details are contained in the following reference -- if you use this
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dataset, please cite:
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Athanasios Tsanas, Max A. Little, Patrick E. McSharry, Lorraine O. Ramig (2009),
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'Accurate telemonitoring of Parkinson.s disease progression by non-invasive
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speech tests',
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IEEE Transactions on Biomedical Engineering (to appear).
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Further details about the biomedical voice measures can be found in:
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Max A. Little, Patrick E. McSharry, Eric J. Hunter, Lorraine O. Ramig (2009),
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'Suitability of dysphonia measurements for telemonitoring of Parkinson's
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disease',
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IEEE Transactions on Biomedical Engineering, 56(4):1015-1022
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ATTRIBUTE INFORMATION:
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subject# - Integer that uniquely identifies each subject
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age - Subject age
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sex - Subject gender '0' - male, '1' - female
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test_time - Time since recruitment into the trial. The integer part is the
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number of days since recruitment.
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motor_UPDRS - Clinician's motor UPDRS score, linearly interpolated
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total_UPDRS - Clinician's total UPDRS score, linearly interpolated
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Jitter(%),Jitter(Abs),Jitter:RAP,Jitter:PPQ5,Jitter:DDP - Several measures of
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variation in fundamental frequency
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Shimmer,Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,Shimmer:APQ11,Shimmer:DDA -
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Several measures of variation in amplitude
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NHR,HNR - Two measures of ratio of noise to tonal components in the voice
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RPDE - A nonlinear dynamical complexity measure
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DFA - Signal fractal scaling exponent
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PPE - A nonlinear measure of fundamental frequency variation
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===========================================================
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RELEVANT PAPERS:
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Little MA, McSharry PE, Hunter EJ, Ramig LO (2009),
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'Suitability of dysphonia measurements for telemonitoring of Parkinson's
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disease',
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IEEE Transactions on Biomedical Engineering, 56(4):1015-1022
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Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM.
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'Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice
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Disorder Detection',
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BioMedical Engineering OnLine 2007, 6:23 (26 June 2007)
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===========================================================
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CITATION REQUEST:
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If you use this dataset, please cite the following paper:
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A Tsanas, MA Little, PE McSharry, LO Ramig (2009)
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'Accurate telemonitoring of Parkinson.s disease progression by non-invasive
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speech tests',
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IEEE Transactions on Biomedical Engineering (to appear).
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