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76 lines
3.0 KiB
Plaintext
Executable File
76 lines
3.0 KiB
Plaintext
Executable File
Title: Parkinsons Disease Data Set
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Abstract: Oxford Parkinson's Disease Detection Dataset
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Data Set Characteristics: Multivariate
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Number of Instances: 197
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Area: Life
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Attribute Characteristics: Real
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Number of Attributes: 23
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Date Donated: 2008-06-26
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Associated Tasks: Classification
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Missing Values? N/A
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Source:
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The dataset was created by Max Little of the University of Oxford, in
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collaboration with the National Centre for Voice and Speech, Denver,
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Colorado, who recorded the speech signals. The original study published the
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feature extraction methods for general voice disorders.
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Data Set Information:
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This dataset is composed of a range of biomedical voice measurements from
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31 people, 23 with Parkinson's disease (PD). Each column in the table is a
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particular voice measure, and each row corresponds one of 195 voice
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recording from these individuals ("name" column). The main aim of the data
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is to discriminate healthy people from those with PD, according to "status"
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column which is set to 0 for healthy and 1 for PD.
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The data is in ASCII CSV format. The rows of the CSV file contain an
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instance corresponding to one voice recording. There are around six
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recordings per patient, the name of the patient is identified in the first
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column.For further information or to pass on comments, please contact Max
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Little (littlem '@' robots.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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Max A. Little, Patrick E. McSharry, Eric J. Hunter, Lorraine O. Ramig (2008),
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'Suitability of dysphonia measurements for telemonitoring of Parkinson's disease',
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IEEE Transactions on Biomedical Engineering (to appear).
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Attribute Information:
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Matrix column entries (attributes):
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name - ASCII subject name and recording number
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MDVP:Fo(Hz) - Average vocal fundamental frequency
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MDVP:Fhi(Hz) - Maximum vocal fundamental frequency
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MDVP:Flo(Hz) - Minimum vocal fundamental frequency
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MDVP:Jitter(%),MDVP:Jitter(Abs),MDVP:RAP,MDVP:PPQ,Jitter:DDP - Several
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measures of variation in fundamental frequency
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MDVP:Shimmer,MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP:APQ,Shimmer:DDA - 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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status - Health status of the subject (one) - Parkinson's, (zero) - healthy
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RPDE,D2 - Two nonlinear dynamical complexity measures
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DFA - Signal fractal scaling exponent
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spread1,spread2,PPE - Three nonlinear measures of fundamental frequency variation
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Citation Request:
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If you use this dataset, please cite the following paper:
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'Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection',
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Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM.
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BioMedical Engineering OnLine 2007, 6:23 (26 June 2007)
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