Parkinsons

Donated on 6/25/2008

Oxford Parkinson's Disease Detection Dataset

Dataset Characteristics

Multivariate

Subject Area

Health and Medicine

Associated Tasks

Classification

Feature Type

Real

# Instances

197

# Features

22

Dataset Information

Additional Information

This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to "status" column which is set to 0 for healthy and 1 for PD. The data is in ASCII CSV format. The rows of the CSV file contain an instance corresponding to one voice recording. There are around six recordings per patient, the name of the patient is identified in the first column.For further information or to pass on comments, please contact Max Little (littlem '@' robots.ox.ac.uk). Further details are contained in the following reference -- if you use this dataset, please cite: Max A. Little, Patrick E. McSharry, Eric J. Hunter, Lorraine O. Ramig (2008), 'Suitability of dysphonia measurements for telemonitoring of Parkinson's disease', IEEE Transactions on Biomedical Engineering (to appear).

Has Missing Values?

No

Introductory Paper

Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection

By Max A. Little, P. McSharry, S. Roberts, D. Costello, I. Moroz. 2007

Published in BioMedical Engineering OnLine

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
nameIDCategoricalno
MDVP:FoFeatureContinuousHzno
MDVP:FhiFeatureContinuousHzno
MDVP:FloFeatureContinuousHzno
MDVP:JitterFeatureContinuous%no
MDVP:JitterFeatureContinuousAbsno
MDVP:RAPFeatureContinuousno
MDVP:PPQFeatureContinuousno
Jitter:DDPFeatureContinuousno
MDVP:ShimmerFeatureContinuousno

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Additional Variable Information

Matrix column entries (attributes): name - ASCII subject name and recording number MDVP:Fo(Hz) - Average vocal fundamental frequency MDVP:Fhi(Hz) - Maximum vocal fundamental frequency MDVP:Flo(Hz) - Minimum vocal fundamental frequency MDVP:Jitter(%),MDVP:Jitter(Abs),MDVP:RAP,MDVP:PPQ,Jitter:DDP - Several measures of variation in fundamental frequency MDVP:Shimmer,MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP:APQ,Shimmer:DDA - Several measures of variation in amplitude NHR,HNR - Two measures of ratio of noise to tonal components in the voice status - Health status of the subject (one) - Parkinson's, (zero) - healthy RPDE,D2 - Two nonlinear dynamical complexity measures DFA - Signal fractal scaling exponent spread1,spread2,PPE - Three nonlinear measures of fundamental frequency variation

Papers Citing this Dataset

Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization

By Aaron Klein, Frank Hutter. 2019

Published in ArXiv.

On the Interaction Effects Between Prediction and Clustering

By Matt Barnes, Artur Dubrawski. 2018

Published in Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019, Volume 89.

PRIL: Perceptron Ranking Using Interval Labeled Data

By Naresh Manwani. 2018

Published in ArXiv.

Cadre Modeling: Simultaneously Discovering Subpopulations and Predictive Models

By Alexander New, Curt Breneman, Kristin Bennett. 2018

Published in In 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 2018.

A Riemannian gossip approach to subspace learning on Grassmann manifold

By Bamdev Mishra, Hiroyuki Kasai, Pratik Jawanpuria, Atul Saroop. 2017

Published in Machine Learning.

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Creators

Max Little

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