Multicondition training for PD diagnosis with voice features from diadochokinesis test

Published: 24 September 2024| Version 1 | DOI: 10.17632/zdp72s54cd.1
Contributors:
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Description

The dataset includes acoustic features extracted from voice recordings using a diadochokinesis (DDK) test protocol. Realistic environmental noise was introduced into the recordings prior to feature extraction to simulate real-world conditions. The dataset comprises voice samples from 30 Parkinson's disease (PD) patients and 30 healthy controls. All samples were captured via smartphone without audio compression to preserve data quality.

Files

Steps to reproduce

This dataset has been derived and used in the paper: Mario Madruga Escalona, Yolanda Campos-Roca, Carlos Javier Pérez Sánchez, Enhancing noise robustness of automatic Parkinson’s disease detection in diadochokinesis tests using multicondition training, Expert Systems with Applications, Volume 260, 125401, 2025, https://doi.org/10.1016/j.eswa.2024.125401

Institutions

Universidad de Extremadura

Categories

Signal Processing, Machine Learning, Diagnosis of Parkinson's Disease, Mobile Health

Funding

Agencia Estatal de Investigación

PID2021-122209OB-C32

Junta de Extremadura

GR21057

Junta de Extremadura

GR21072

Ministerio de Ciencia, Innovación y Universidades

FPU18/03274

Licence