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
By A. Tsanas, Max A. Little, P. McSharry, L. Ramig. 2010
Published in IEEE Transactions on Biomedical Engineering
Variables Table
| Variable Name | Role | Type | Demographic | Description | Units | Missing Values |
|---|---|---|---|---|---|---|
| subject# | ID | Integer | Integer that uniquely identifies each subject | no | ||
| age | Feature | Integer | Age | Subject age | no | |
| test_time | Feature | Continuous | Time since recruitment into the trial. The integer part is the number of days since recruitment. | no | ||
| Jitter(%) | Feature | Continuous | Several measures of variation in fundamental frequency | no | ||
| Jitter(Abs) | Feature | Continuous | Several measures of variation in fundamental frequency | no | ||
| Jitter:RAP | Feature | Continuous | Several measures of variation in fundamental frequency | no | ||
| Jitter:PPQ5 | Feature | Continuous | Several measures of variation in fundamental frequency | no | ||
| Jitter:DDP | Feature | Continuous | Several measures of variation in fundamental frequency | no | ||
| Shimmer | Feature | Continuous | Several measures of variation in amplitude | no | ||
| Shimmer(dB) | Feature | Continuous | Several measures of variation in amplitude | no |
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
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset parkinsons_telemonitoring = fetch_ucirepo(id=189) # data (as pandas dataframes) X = parkinsons_telemonitoring.data.features y = parkinsons_telemonitoring.data.targets # metadata print(parkinsons_telemonitoring.metadata) # variable information print(parkinsons_telemonitoring.variables)
Tsanas,Athanasios and Little,Max. (2009). Parkinsons Telemonitoring. UCI Machine Learning Repository. https://doi.org/10.24432/C5ZS3N.
@misc{misc_parkinsons_telemonitoring_189,
author = {Tsanas,Athanasios and Little,Max},
title = {{Parkinsons Telemonitoring}},
year = {2009},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5ZS3N}
}Creators
Athanasios Tsanas
Max Little
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.