Cargando…

A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions

BACKGROUND AND OBJECTIVE: Automatic voice condition analysis systems to detect Parkinson’s disease (PD) are generally based on speech data recorded under acoustically controlled conditions and professional supervision. The performance of these approaches in a free-living scenario is unknown. The aim...

Descripción completa

Detalles Bibliográficos
Autores principales: Carrón, Javier, Campos-Roca, Yolanda, Madruga, Mario, Pérez, Carlos J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8607631/
https://www.ncbi.nlm.nih.gov/pubmed/34802448
http://dx.doi.org/10.1186/s12938-021-00951-y
_version_ 1784602598536380416
author Carrón, Javier
Campos-Roca, Yolanda
Madruga, Mario
Pérez, Carlos J.
author_facet Carrón, Javier
Campos-Roca, Yolanda
Madruga, Mario
Pérez, Carlos J.
author_sort Carrón, Javier
collection PubMed
description BACKGROUND AND OBJECTIVE: Automatic voice condition analysis systems to detect Parkinson’s disease (PD) are generally based on speech data recorded under acoustically controlled conditions and professional supervision. The performance of these approaches in a free-living scenario is unknown. The aim of this research is to investigate the impact of uncontrolled conditions (realistic acoustic environment and lack of supervision) on the performance of automatic PD detection systems based on speech. METHODS: A mobile-assisted voice condition analysis system is proposed to aid in the detection of PD using speech. The system is based on a server–client architecture. In the server, feature extraction and machine learning algorithms are designed and implemented to discriminate subjects with PD from healthy ones. The Android app allows patients to submit phonations and physicians to check the complete record of every patient. Six different machine learning classifiers are applied to compare their performance on two different speech databases. One of them is an in-house database (UEX database), collected under professional supervision by using the same Android-based smartphone in the same room, whereas the other one is an age, sex and health-status balanced subset of mPower study for PD, which provides real-world data. By applying identical methodology, single-database experiments have been performed on each database, and also cross-database tests. Cross-validation has been applied to assess generalization performance and hypothesis tests have been used to report statistically significant differences. RESULTS: In the single-database experiments, a best accuracy rate of 0.92 (AUC = 0.98) has been obtained on UEX database, while a considerably lower best accuracy rate of 0.71 (AUC = 0.76) has been achieved using the mPower-based database. The cross-database tests provided very degraded accuracy metrics. CONCLUSION: The results clearly show the potential of the proposed system as an aid for general practitioners to conduct triage or an additional tool for neurologists to perform diagnosis. However, due to the performance degradation observed using data from mPower study, semi-controlled conditions are encouraged, i.e., voices recorded at home by the patients themselves following a strict recording protocol and control of the information about patients by the medical doctor at charge.
format Online
Article
Text
id pubmed-8607631
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-86076312021-11-22 A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions Carrón, Javier Campos-Roca, Yolanda Madruga, Mario Pérez, Carlos J. Biomed Eng Online Research BACKGROUND AND OBJECTIVE: Automatic voice condition analysis systems to detect Parkinson’s disease (PD) are generally based on speech data recorded under acoustically controlled conditions and professional supervision. The performance of these approaches in a free-living scenario is unknown. The aim of this research is to investigate the impact of uncontrolled conditions (realistic acoustic environment and lack of supervision) on the performance of automatic PD detection systems based on speech. METHODS: A mobile-assisted voice condition analysis system is proposed to aid in the detection of PD using speech. The system is based on a server–client architecture. In the server, feature extraction and machine learning algorithms are designed and implemented to discriminate subjects with PD from healthy ones. The Android app allows patients to submit phonations and physicians to check the complete record of every patient. Six different machine learning classifiers are applied to compare their performance on two different speech databases. One of them is an in-house database (UEX database), collected under professional supervision by using the same Android-based smartphone in the same room, whereas the other one is an age, sex and health-status balanced subset of mPower study for PD, which provides real-world data. By applying identical methodology, single-database experiments have been performed on each database, and also cross-database tests. Cross-validation has been applied to assess generalization performance and hypothesis tests have been used to report statistically significant differences. RESULTS: In the single-database experiments, a best accuracy rate of 0.92 (AUC = 0.98) has been obtained on UEX database, while a considerably lower best accuracy rate of 0.71 (AUC = 0.76) has been achieved using the mPower-based database. The cross-database tests provided very degraded accuracy metrics. CONCLUSION: The results clearly show the potential of the proposed system as an aid for general practitioners to conduct triage or an additional tool for neurologists to perform diagnosis. However, due to the performance degradation observed using data from mPower study, semi-controlled conditions are encouraged, i.e., voices recorded at home by the patients themselves following a strict recording protocol and control of the information about patients by the medical doctor at charge. BioMed Central 2021-11-21 /pmc/articles/PMC8607631/ /pubmed/34802448 http://dx.doi.org/10.1186/s12938-021-00951-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Carrón, Javier
Campos-Roca, Yolanda
Madruga, Mario
Pérez, Carlos J.
A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions
title A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions
title_full A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions
title_fullStr A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions
title_full_unstemmed A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions
title_short A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions
title_sort mobile-assisted voice condition analysis system for parkinson’s disease: assessment of usability conditions
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8607631/
https://www.ncbi.nlm.nih.gov/pubmed/34802448
http://dx.doi.org/10.1186/s12938-021-00951-y
work_keys_str_mv AT carronjavier amobileassistedvoiceconditionanalysissystemforparkinsonsdiseaseassessmentofusabilityconditions
AT camposrocayolanda amobileassistedvoiceconditionanalysissystemforparkinsonsdiseaseassessmentofusabilityconditions
AT madrugamario amobileassistedvoiceconditionanalysissystemforparkinsonsdiseaseassessmentofusabilityconditions
AT perezcarlosj amobileassistedvoiceconditionanalysissystemforparkinsonsdiseaseassessmentofusabilityconditions
AT carronjavier mobileassistedvoiceconditionanalysissystemforparkinsonsdiseaseassessmentofusabilityconditions
AT camposrocayolanda mobileassistedvoiceconditionanalysissystemforparkinsonsdiseaseassessmentofusabilityconditions
AT madrugamario mobileassistedvoiceconditionanalysissystemforparkinsonsdiseaseassessmentofusabilityconditions
AT perezcarlosj mobileassistedvoiceconditionanalysissystemforparkinsonsdiseaseassessmentofusabilityconditions