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

Abstract 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....

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Autores principales:	Javier Carrón, Yolanda Campos-Roca, Mario Madruga, Carlos J. Pérez
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Acoustic features Machine learning mPower database Parkinson’s disease Speech processing Voice condition analysis system Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/73cf576c118547fe8a114750d9aee66b
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spelling	oai:doaj.org-article:73cf576c118547fe8a114750d9aee66b2021-11-28T12:13:21ZA mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions10.1186/s12938-021-00951-y1475-925Xhttps://doaj.org/article/73cf576c118547fe8a114750d9aee66b2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12938-021-00951-yhttps://doaj.org/toc/1475-925XAbstract 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.Javier CarrónYolanda Campos-RocaMario MadrugaCarlos J. PérezBMCarticleAcoustic featuresMachine learningmPower databaseParkinson’s diseaseSpeech processingVoice condition analysis systemMedical technologyR855-855.5ENBioMedical Engineering OnLine, Vol 20, Iss 1, Pp 1-24 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Acoustic features Machine learning mPower database Parkinson’s disease Speech processing Voice condition analysis system Medical technology R855-855.5
spellingShingle	Acoustic features Machine learning mPower database Parkinson’s disease Speech processing Voice condition analysis system Medical technology R855-855.5 Javier Carrón Yolanda Campos-Roca Mario Madruga Carlos J. Pérez A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions
description	Abstract 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	article
author	Javier Carrón Yolanda Campos-Roca Mario Madruga Carlos J. Pérez
author_facet	Javier Carrón Yolanda Campos-Roca Mario Madruga Carlos J. Pérez
author_sort	Javier Carrón
title	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_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_sort	mobile-assisted voice condition analysis system for parkinson’s disease: assessment of usability conditions
publisher	BMC
publishDate	2021
url	https://doaj.org/article/73cf576c118547fe8a114750d9aee66b
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A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions

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