Parkinsons Telemonitoring Data Set(帕金森远程监测
数据集)
数据摘要：
Oxford Parkinson Dia Telemonitoring Datat. This datat is compod of a range of biomedical voice measurements from 42 people with early-stage Parkinson's dia 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 baline recruitment date, motor UPDRS, total UPDRS, and 16 biomedical voice measures. Each row corresponds to one of 5,875 voice recording from the 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.
中文关键词：
帕金森病，远程监测，数据集，牛津，生物医学声音
测量,
英文关键词：
Parkinson,Telemonitoring,Datat,Oxford,biomedical voice measurements,
数据格式：
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数据用途：
Regression
数据详细介绍：开天辟地
Parkinsons Telemonitoring Data Set液态地膜
Abstract: Oxford Parkinson's Dia Telemonitoring Datat
Source:
The datat was created by Athanasios Tsanas (tsanasthanasis '@' ) and Max Little (littlem '@' ac.uk) of the University of Oxford, in collaboration with 10 medical centers in the US and Intel Corporation who developed the telemonitoring device to record the speech signals. The original study ud a range of linear and nonlinear regression methods to predict the clinician's Parkinson's dia symptom score on the UPDRS scale.
Data Set Information:
This datat is compod of a range of biomedical voice measurements from 42 people with early-st
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age Parkinson's dia 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 baline recruitment date, motor UPDRS, total UPDRS, and 16 biomedical voice measures. Each row corresponds to one of 5,875 voice recording from the 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.贫困证明申请书
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, plea contact Athanasios Tsanas (tsanasthanasis '@' ) or Max Little (littlem '@' ac.uk).
Further details are contained in the following reference -- if you u this datat, plea cite: Athanasios Tsanas, Max A. Little, Patrick E. McSharry, Lorraine O. Ramig (2009),
'Accurate telemonitoring of Parkinson?s dia 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 dia',
IEEE Transactions on Biomedical Engineering, 56(4):1015-1022
Attribute Information:
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subject# - Integer that uniquely identifies each subject
age - Subject age
x - 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 noi to tonal components in the voice
RPDE - A nonlinear dynamical complexity measure
DFA - Signal fractal scaling exponent
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PPE - A nonlinear measure of fundamental frequency variation
Relevant Papers:
Little MA, McSharry PE, Hunter EJ, Ramig LO (2009),
'Suitability of dysphonia measurements for telemonitoring of Parkinson's dia',
IEEE Transactions on Biomedical Engineering, 56(4):1015-1022
Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM.
'Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', BioMedical Engineering OnLine 2007, 6:23 (26 June 2007)
Citation Request:
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If you u this datat, plea cite the following paper:
A Tsanas, MA Little, PE McSharry, LO Ramig (2009)
'Accurate telemonitoring of Parkinson?s dia progression by non-invasive speech tests', IEEE Transactions on Biomedical Engineering (to appear).
数据预览：
