New insights into Parkinson’s disease through statistical analysis of standard clinical scales quantifying symptom severity

Tsanas, Athanasios

doi:10.1109/embc.2019.8856559

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Crucially for the purposes of this study, speech as an item within comprehensive PD clinical scales has been previously shown to be very strongly associated with overall PD symptom severity as assessed using standardized clinical metrics [17,18]. Furthermore, in the context of PD assessment we have previously used sustained vowel /a/ phonation to demonstrate: (1) differentiating a control group from PwP with almost 99% accuracy [19], (2) accurately replicating the standard clinical metric used to assess overall PD symptom severity (Unified Parkinson's Disease Rating Scale (UPDRS)) [20][21][22], (3) automatically assessing voice rehabilitation [23]; (4) distinguishing PwP from controls and groups which are known to exhibit genetic mutations…”

Section: Introductionmentioning

confidence: 98%

Data-Driven Subtyping of Parkinson’s Using Acoustic Analysis of Sustained Vowels and Cluster Analysis: Findings in the Parkinson’s Voice Initiative Study

Tsanas

Arora

2022

SN COMPUT. SCI.

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People diagnosed with Parkinson’s (PwP) exhibit a diverse manifestation of heterogeneous symptoms which likely reflect different subtypes. However, there is no widely accepted consensus on the criteria for subtype membership assignment. We explored clustering PwP using a data-driven approach mining speech signals. We used data from the three English-speaking cohorts (Boston, Oxford, Toronto) in the Parkinson’s Voice Initiative (PVI), where speech and basic demographic information were collected over the standard telephone network. We acoustically characterized 2097 sustained vowel /a/ recordings from 1138 PwP (Boston cohort) using 307 dysphonia measures. We applied unsupervised feature selection to select a concise subset of the dysphonia measures and hierarchical clustering combined with 2D-data projections using t-distributed stochastic neighbor embedding (t-SNE) to facilitate visual exploration of PwP groups. We assessed cluster validity and consistency using silhouette plots and the cophenetic correlation coefficient. We externally validated cluster findings on the Oxford and Toronto PVI cohorts (n = 285 and 107 participants, respectively). We selected 21 dysphonia measures and found four main clusters which provide tentative insights into different dominating speech-associated characteristics (cophenetic coefficient = 0.72, silhouette score = 0.67). The cluster findings were consistent across the three PVI cohorts, strongly supporting the generalization of the presented methodology towards PwP subtype assignment, and were independently visually verified in 2D projections with t-SNE. The presented methodology with mining sustained vowels and clustering may provide an objective and streamlined approach towards informing PwP subtype assignment. This may have important implications towards developing more personalized clinical management of symptoms for PwP.

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Section: Introductionmentioning

confidence: 98%

Data-Driven Subtyping of Parkinson’s Using Acoustic Analysis of Sustained Vowels and Cluster Analysis: Findings in the Parkinson’s Voice Initiative Study

Tsanas

Arora

2022

SN COMPUT. SCI.

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“…The motor-UPDRS ranges between 0 and 108, where 0 denotes healthy state, and 108 severe disabilities, and the total-UPDRS lies in the range between 0 and 176. In addition to UPDRS, the Hoehn and Yahr (H&Y) scale is often used, and it is possible to infer H&Y from UPDRS [10] , [11] . Other clinical scales are sometimes used in some medical centers, but for the purposes of this study we shall confine our analysis exclusively on UPDRS.…”

Section: Introductionmentioning

confidence: 99%

Remote Assessment of Parkinson’s Disease Symptom Severity Using the Simulated Cellular Mobile Telephone Network

2021

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Parkinson’s Disease Detection by Using Feature Selection and Sparse Representation

et al. 2021

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New insights into Parkinson’s disease through statistical analysis of standard clinical scales quantifying symptom severity

Cited by 5 publications

References 18 publications

Data-Driven Subtyping of Parkinson’s Using Acoustic Analysis of Sustained Vowels and Cluster Analysis: Findings in the Parkinson’s Voice Initiative Study

Data-Driven Subtyping of Parkinson’s Using Acoustic Analysis of Sustained Vowels and Cluster Analysis: Findings in the Parkinson’s Voice Initiative Study

Remote Assessment of Parkinson’s Disease Symptom Severity Using the Simulated Cellular Mobile Telephone Network

Parkinson’s Disease Detection by Using Feature Selection and Sparse Representation

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