
Early biomarkers of Parkinson’s disease based on natural connected speech
Donated on 2/14/2017
Predict a pattern of neurodegeneration in the dataset of speech features obtained from patients with early untreated Parkinson’s disease and patients at high risk developing Parkinson’s disease.
Dataset Characteristics
Multivariate
Subject Area
Health and Medicine
Associated Tasks
Classification, Regression
Feature Type
Integer, Real
# Instances
130
# Features
-
Dataset Information
Additional Information
The dataset include 30 patients with early untreated Parkinson’s disease (PD), 50 patients with REM sleep behaviour disorder (RBD), which are at high risk developing Parkinson’s disease or other synucleinopathies; and 50 healthy controls (HC). All patients were scored clinically by a well-trained professional neurologist with experience in movement disorders. All subjects were examined during a single session with a speech specialist. All subjects performed reading of standardized, phonetically-balanced text of 80 words and monologue about their interests, job, family or current activities for approximately 90 seconds. Speech features were automatically analyzed by HlavniÄka et al. (2017). The dataset is provided in xls and csv format. The table is organized as follows: • First two rows include header • First row denote sections (Demographic information, Clinical information, etc.) • Second row denote attribute • Each row in range from 3 to 132 corresponds to one particular subject identified by a unique code (first column). The code indicate also clinical diagnosis of the subject (PD, RBD, or HC) For further details about the computational procedures, please see HlavniÄka (2017), or contact rusz.mz (at) gmail.com.
Has Missing Values?
Yes
Variables Table
Variable Name | Role | Type | Description | Units | Missing Values |
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no | |||||
no | |||||
no | |||||
no | |||||
no |
0 to 10 of 65
Additional Variable Information
• Participant code = unique code for identification of a subject and diagnosis (PD = Parkinson’s disease, RBD = rapid eye movement sleep behaviour disorder, HC = Healthy control) • UPDRS III = Unified Parkinson’s Disease Rating Scale III, accordingly to: Stebbins, G. T. & Goetz, C. G. Factor structure of the Unified Parkinson’s Disease Rating Scale: motor examination section. Mov. Disord 13, 633–636 (1998). • Gender 'F' = female • Gender 'M' = male • Speech examination: speaking task of reading passage = speech features analyzed on reading passage • Speech examination: speaking task of monologue = speech features analyzed on monologue For further details about the analyzed speech features, please see HlavniÄka (2017), or contact rusz.mz (at) gmail.com.
Dataset Files
File | Size |
---|---|
DATASET.ZIP | 64 KB |
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset early_biomarkers_of_parkinson_s_disease_based_on_natural_connected_speech = fetch_ucirepo(id=392) # data (as pandas dataframes) X = early_biomarkers_of_parkinson_s_disease_based_on_natural_connected_speech.data.features y = early_biomarkers_of_parkinson_s_disease_based_on_natural_connected_speech.data.targets # metadata print(early_biomarkers_of_parkinson_s_disease_based_on_natural_connected_speech.metadata) # variable information print(early_biomarkers_of_parkinson_s_disease_based_on_natural_connected_speech.variables)
Hlavnika, J., Tykalov, T., Onka, K., Rika, E., Rusz, J., & J., J. (2017). Early biomarkers of Parkinson’s disease based on natural connected speech [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5W02Q.
Creators
J. Hlavnika
T. Tykalov
K. Onka
E. Rika
J. Rusz
J. J.
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.