parkinsons: Parkinsons

In tyluRp/ucimlr: UCI Machine Learning Repository

Description

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).

Usage

parkinsons

Format

A data frame with 5875 observations on the following 22 variables.

1. subject: Integer that uniquely identifies each subject

2. age: Subject age

3. sex: Subject gender, original coded as 0/1 now male/female

4. test_time: Time since recruitment into the trial. The integer part is the number of days since recruitment.

5. motor_updrs: Clinician's motor UPDRS score, linearly interpolated

6. total_updrs: Clinician's total UPDRS score, linearly interpolated

7. jitter: KP-MDVP jitter as a percentage

8. jitter_abs: KP-MDVP absolute jitter in microseconds

9. jitter_rap: KP-MDVP Relative Amplitude Perturbation

10. jitter_ppq5: KP-MDVP five-point Period Perturbation Quotient

11. jitter_ddp: Average absolute difference of differences between cycles, divided by the average period

12. shimmer: KP-MDVP local shimmer

13. shimmer_d_b: KP-MDVP local shimmer in decibels

14. shimmer_apq3: Three point Amplitude Perturbation Quotient

15. shimmer_apq5: Five point Amplitude Perturbation Quotient

16. shimmer_apq11: KP-MDVP 11-point Amplitude Perturbation Quotient

17. shimmer_dda: Average absolute difference between consecutive differences between the amplitudes of consecutive periods

18. nhr: Noise-to-Harmonics Ratio

19. hnr: Harmonics-to-Noise Ratio

20. rpde: Recurrence Period Density Entropy

21. dfa: Detrended Fluctuation Analysis

22. ppe: Pitch Period Entropy

Details

Oxford Parkinson's Disease Telemonitoring Dataset.

Source

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.

References

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

tyluRp/ucimlr documentation built on Feb. 2, 2021, 6:51 a.m.