Parkinsons Telemonitoring

Donated on 10/28/2009

Oxford Parkinson's Disease Telemonitoring Dataset

Dataset Characteristics

Tabular

Subject Area

Health and Medicine

Associated Tasks

Regression

Feature Type

Integer, Real

# Instances

5875

# Features

19

Dataset Information

Additional Information

This dataset is composed of a range of biomedical voice measurements from 42 people with early-stage Parkinson's disease recruited to a six-month trial of a telemonitoring device for remote symptom progression monitoring. The recordings were automatically captured in the patient's homes. Columns in the table contain subject number, subject age, subject gender, time interval from baseline recruitment date, motor UPDRS, total UPDRS, and 16 biomedical voice measures. Each row corresponds to one of 5,875 voice recording from these individuals. The main aim of the data is to predict the motor and total UPDRS scores ('motor_UPDRS' and 'total_UPDRS') from the 16 voice measures. 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

Has Missing Values?

No

Introductory Paper

Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech Tests

By A. Tsanas, Max A. Little, P. McSharry, L. Ramig. 2010

Published in IEEE Transactions on Biomedical Engineering

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
subject#IDIntegerInteger that uniquely identifies each subjectno
ageFeatureIntegerAgeSubject ageno
test_timeFeatureContinuousTime since recruitment into the trial. The integer part is the number of days since recruitment. no
Jitter(%)FeatureContinuousSeveral measures of variation in fundamental frequencyno
Jitter(Abs)FeatureContinuousSeveral measures of variation in fundamental frequencyno
Jitter:RAPFeatureContinuousSeveral measures of variation in fundamental frequencyno
Jitter:PPQ5FeatureContinuousSeveral measures of variation in fundamental frequencyno
Jitter:DDPFeatureContinuousSeveral measures of variation in fundamental frequencyno
ShimmerFeatureContinuousSeveral measures of variation in amplitudeno
Shimmer(dB)FeatureContinuousSeveral measures of variation in amplitudeno

0 to 10 of 22

Additional Variable 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

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Creators

Athanasios Tsanas

Max Little

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