Oxford Parkinson's Disease Telemonitoring Dataset. 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).
parkinsons
A data frame with 5875 observations on the following 22 variables.
subject: Integer that uniquely identifies each subject
age: Subject age
sex: Subject gender, original coded as 0/1 now male/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: KP-MDVP jitter as a percentage
jitter_abs: KP-MDVP absolute jitter in microseconds
jitter_rap: KP-MDVP Relative Amplitude Perturbation
jitter_ppq5: KP-MDVP five-point Period Perturbation Quotient
jitter_ddp: Average absolute difference of differences between cycles, divided by the average period
shimmer: KP-MDVP local shimmer
shimmer_d_b: KP-MDVP local shimmer in decibels
shimmer_apq3: Three point Amplitude Perturbation Quotient
shimmer_apq5: Five point Amplitude Perturbation Quotient
shimmer_apq11: KP-MDVP 11-point Amplitude Perturbation Quotient
shimmer_dda: Average absolute difference between consecutive differences between the amplitudes of consecutive periods
nhr: Noise-to-Harmonics Ratio
hnr: Harmonics-to-Noise Ratio
rpde: Recurrence Period Density Entropy
dfa: Detrended Fluctuation Analysis
ppe: Pitch Period Entropy
The dataset was created by Athanasios Tsanas (tsanasthanasis '@' gmail.com) and Max Little (littlem '@' physics.ox.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 used a range of linear and nonlinear regression methods to predict the clinician's Parkinson's disease symptom score on the UPDRS scale.
Oxford Parkinson's Disease Telemonitoring Dataset.
A Tsanas, MA Little, PE McSharry, LO Ramig (2009) 'Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests', IEEE Transactions on Biomedical Engineering
Little MA, McSharry PE, Hunter EJ, Ramig LO (2009), 'Suitability of dysphonia measurements for telemonitoring of Parkinson's disease', 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)
https://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/telemonitoring/
https://archive.ics.uci.edu/ml/datasets/Parkinsons+Telemonitoring