Title: Parkinsons Disease Data Set

Abstract: Oxford Parkinson's Disease Detection Dataset

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Data Set Characteristics: Multivariate

Number of Instances: 197

Area: Life

Attribute Characteristics: Real

Number of Attributes: 23

Date Donated: 2008-06-26

Associated Tasks: Classification

Missing Values? N/A

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Source:

The dataset was created by Max Little of the University of Oxford, in 

collaboration with the National Centre for Voice and Speech, Denver, 

Colorado, who recorded the speech signals. The original study published the 

feature extraction methods for general voice disorders.

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Data Set 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).

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Attribute 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 

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Citation Request:

If you use this dataset, please cite the following paper: 

'Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', 

Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. 

BioMedical Engineering OnLine 2007, 6:23 (26 June 2007)