Investigating voice as a biomarker: Deep phenotyping methods for early detection of Parkinson's disease

Tracy, John M.; Özkanca, Yasin; Atkins, David C.; Ghomi, Reza Hosseini

doi:10.1016/j.jbi.2019.103362

Cited by 141 publications

(87 citation statements)

References 22 publications

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“…Moreover, the accuracy of their results based on SVM and ANN were both lower than ours. Similar work that is based on the above features can be found in Tracy et al [ 28 ]. Deep learning methods have also been leveraged to learn patterns from vocal feature sets [ 29 ].…”

Section: Discussionmentioning

confidence: 61%

An Intelligent Mobile-Enabled System for Diagnosing Parkinson Disease: Development and Validation of a Speech Impairment Detection System

Zhang¹,

Qu²,

Jin³

et al. 2020

JMIR Med Inform

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Background Parkinson disease (PD) is one of the most common neurological diseases. At present, because the exact cause is still unclear, accurate diagnosis and progression monitoring remain challenging. In recent years, exploring the relationship between PD and speech impairment has attracted widespread attention in the academic world. Most of the studies successfully validated the effectiveness of some vocal features. Moreover, the noninvasive nature of speech signal–based testing has pioneered a new way for telediagnosis and telemonitoring. In particular, there is an increasing demand for artificial intelligence–powered tools in the digital health era. Objective This study aimed to build a real-time speech signal analysis tool for PD diagnosis and severity assessment. Further, the underlying system should be flexible enough to integrate any machine learning or deep learning algorithm. Methods At its core, the system we built consists of two parts: (1) speech signal processing: both traditional and novel speech signal processing technologies have been employed for feature engineering, which can automatically extract a few linear and nonlinear dysphonia features, and (2) application of machine learning algorithms: some classical regression and classification algorithms from the machine learning field have been tested; we then chose the most efficient algorithms and relevant features. Results Experimental results showed that our system had an outstanding ability to both diagnose and assess severity of PD. By using both linear and nonlinear dysphonia features, the accuracy reached 88.74% and recall reached 97.03% in the diagnosis task. Meanwhile, mean absolute error was 3.7699 in the assessment task. The system has already been deployed within a mobile app called No Pa. Conclusions This study performed diagnosis and severity assessment of PD from the perspective of speech order detection. The efficiency and effectiveness of the algorithms indirectly validated the practicality of the system. In particular, the system reflects the necessity of a publicly accessible PD diagnosis and assessment system that can perform telediagnosis and telemonitoring of PD. This system can also optimize doctors’ decision-making processes regarding treatments.

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Section: Discussionmentioning

confidence: 61%

An Intelligent Mobile-Enabled System for Diagnosing Parkinson Disease: Development and Validation of a Speech Impairment Detection System

Zhang¹,

Qu²,

Jin³

et al. 2020

JMIR Med Inform

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“…Trace et al applied conventional machine learning methods on voice signals to differentiate participants with Parkinson's disease (PD) who exhibit little to no symptoms from healthy controls. They confirmed that voice may serve as a deep phenotype for Parkinson's disease [26].…”

Section: Disease Subtyping Using Novel Data Sources and Temporal Reasmentioning

confidence: 67%

“…First author NLP STND DL SRC SUB Disease Focus eMERGE OHDSI [14] Datta, S X [15] Liu, Q X X [16] Lyudovyk, O X X X Cancer X [17] Liu, C X X X [18] Hong, N X [19] Shang, N X X X [20] Hripcsak, G X X X [21] Ostropolets, A X X X [22] Reps, J X X X [23] Swerdel, J X X [24] Warner, J X Cancer X [25] Shen, F X X X X [26] Trace, JM X Parkinson's [27] Mate, S X [28] Meng, W Cancer [29] Zhao, J X X Cardiovascular X [30] Chen, X X X Rare disease [31] Xu, Z X X X Acute kidney injury [32] Zhang, L X X [33] Chen, P X TOTAL 6 12 6 6 3 5 6…”

Section: Referencementioning

confidence: 99%

Deep phenotyping: Embracing complexity and temporality—Towards scalability, portability, and interoperability

Weng

Shah

Hripcsak

2020

Journal of Biomedical Informatics

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Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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“…Numerous DB have been proposed for PD diagnosis as well as to establish a link with clinical rating scales such as UPDRS and to quantify disease severity or intervention effect [4][5][6][7][8] . Despite these various proof of concept studies, many technical challenges with respect to DB deployment remain unaddressed.…”

Section: Introductionmentioning

confidence: 99%

Exploring test retest reliability and longitudinal stability of digital biomarkers for Parkinson’s disease in the m-Power dataset

Far

Eickhoff

Goñi

et al. 2020

Preprint

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BackgroundDigital biomarkers (DB) as captured using sensors embedded in modern smart devices are a promising technology for home-based symptom monitoring in Parkinson’s disease (PD).ObjectiveDespite extensive application in recent studies test-retest reliability and longitudinal stability of DB has not been well addressed in this context. We utilized the large-scale m-Power dataset to establish the test-retest reliability and longitudinal stability of gait, balance, voice and tapping tasks in an unsupervised and self-administered daily life setting in PD patients and healthy volunteers.MethodsIntraclass Correlation Coefficients (ICC) were computed to estimate the test-retest reliability of features that also differentiate between PD and healthy volunteers. In addition, we tested for longitudinal stability of DB measures in PD and HC as well as for their sensitivity to PD medication effects.ResultsAmong the features differing between PD and HC, only few tapping and voice features had good to excellent test-retest reliabilities and medium to large effect sizes. All other features performed poorly in this respect. Only few features were sensitive to medication effects. The longitudinal analyses revealed significant alterations over time across a variety of features and in particular for the tapping task.ConclusionsThese results indicate the need for further development of more standardized, sensitive and reliable DB for application in self-administered remote studies in PD patients. Motivational, learning and other confounds may cause a variation in performance that needs to be considered in DB longitudinal applications.

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Investigating voice as a biomarker: Deep phenotyping methods for early detection of Parkinson's disease

Cited by 141 publications

References 22 publications

An Intelligent Mobile-Enabled System for Diagnosing Parkinson Disease: Development and Validation of a Speech Impairment Detection System

An Intelligent Mobile-Enabled System for Diagnosing Parkinson Disease: Development and Validation of a Speech Impairment Detection System

Deep phenotyping: Embracing complexity and temporality—Towards scalability, portability, and interoperability

Exploring test retest reliability and longitudinal stability of digital biomarkers for Parkinson’s disease in the m-Power dataset

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