Fostering Voice Objective Analysis in Patients with Movement Disorders

Asci, Francesco; Costantini, Giovanni; Saggio, Giovanni; Suppa, Antonio

doi:10.1002/mds.28537

Cited by 13 publications

(5 citation statements)

References 5 publications

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Section: Discussionmentioning

confidence: 95%

“…Moreover, since all early-stage patients were also drug-naïve, we excluded possible confounding on voice recordings from chronic treatment with L-Dopa thus allowing the objective and automatic recognition of PDrelated voice disorders per se. Concerning the specific speech tasks, we compared the sustained emission of a vowel and a sentence by using standardized procedures (11,(17)(18)(19)22, The clinical observation that 84% of the PD patients (68% of early-stage and 100% of mid-advanced-stage patients) manifested voice impairment (UPDRS-III-v ≥1), agrees with the estimated prevalence of hypokinetic dysarthria in PD, which ranges from 70 to 90% (1)(2)(3)(4)44). Furthermore, the severity of voice impairment correlated with disease duration and the overall motor disability and severity, and finally, with the degree of cognitive impairment in PD.…”

Section: Discussionmentioning

confidence: 99%

“…The human voice however, represents a complex phenomenon characterized by high-dimensional data based on an exponential number of features. Accordingly, besides the independent examination through spectral analysis of specific voice features (i.e., fundamental frequency), more advanced techniques able to analyse and dynamically combine and high-dimensional datasets of voice features such as machine-learning algorithms (17)(18)(19)(20)(21)(22)(23) would improve significantly the accuracy of the objective classification of voice samples in PD. Indeed, machine learning has allowed to classify voice impairment objectively and automatically in a number of neurologic disorders, with previously unreported high accuracy (19,21,22).…”

Section: Introductionmentioning

confidence: 99%

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Voice in Parkinson's Disease: A Machine Learning Study

et al. 2022

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IntroductionParkinson's disease (PD) is characterized by specific voice disorders collectively termed hypokinetic dysarthria. We here investigated voice changes by using machine learning algorithms, in a large cohort of patients with PD in different stages of the disease, OFF and ON therapy.MethodsWe investigated 115 patients affected by PD (mean age: 68.2 ± 9.2 years) and 108 age-matched healthy subjects (mean age: 60.2 ± 11.0 years). The PD cohort included 57 early-stage patients (Hoehn &Yahr ≤ 2) who never took L-Dopa for their disease at the time of the study, and 58 mid-advanced-stage patients (Hoehn &Yahr >2) who were chronically-treated with L-Dopa. We clinically evaluated voices using specific subitems of the Unified Parkinson's Disease Rating Scale and the Voice Handicap Index. Voice samples recorded through a high-definition audio recorder underwent machine learning analysis based on the support vector machine classifier. We also calculated the receiver operating characteristic curves to examine the diagnostic accuracy of the analysis and assessed possible clinical-instrumental correlations.ResultsVoice is abnormal in early-stage PD and as the disease progresses, voice increasingly degradres as demonstrated by high accuracy in the discrimination between healthy subjects and PD patients in the early-stage and mid-advanced-stage. Also, L-dopa therapy improves but not restore voice in PD as shown by high accuracy in the comparison between patients OFF and ON therapy. Finally, for the first time we achieved significant clinical-instrumental correlations by using a new score (LR value) calculated by machine learning.ConclusionVoice is abnormal in early-stage PD, progressively degrades in mid-advanced-stage and can be improved but not restored by L-Dopa. Lastly, machine learning allows tracking disease severity and quantifying the symptomatic effect of L-Dopa on voice parameters with previously unreported high accuracy, thus representing a potential new biomarker of PD.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Voice in Parkinson's Disease: A Machine Learning Study

et al. 2022

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“…During each recording session, subjects were asked to sit in a relaxed position and sustain the vowel /e/ for 5 s at a comfortable volume [ 5 ]. The sustained emission of a vowel could be the most appropriate technical solution for preventing linguistic confounding and achieving standardized worldwide procedures [ 59 ]. Moreover, studies concerning neurological disease (including PD) reported that the results derived from using a sustained vowel are comparable to those obtained with connected speech [ 21 , 32 , 60 ].…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison

Costantini

Cesarini

Leo

et al. 2023

Sensors

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Parkinson’s Disease (PD) is one of the most common non-curable neurodegenerative diseases. Diagnosis is achieved clinically on the basis of different symptoms with considerable delays from the onset of neurodegenerative processes in the central nervous system. In this study, we investigated early and full-blown PD patients based on the analysis of their voice characteristics with the aid of the most commonly employed machine learning (ML) techniques. A custom dataset was made with hi-fi quality recordings of vocal tasks gathered from Italian healthy control subjects and PD patients, divided into early diagnosed, off-medication patients on the one hand, and mid-advanced patients treated with L-Dopa on the other. Following the current state-of-the-art, several ML pipelines were compared usingdifferent feature selection and classification algorithms, and deep learning was also explored with a custom CNN architecture. Results show how feature-based ML and deep learning achieve comparable results in terms of classification, with KNN, SVM and naïve Bayes classifiers performing similarly, with a slight edge for KNN. Much more evident is the predominance of CFS as the best feature selector. The selected features act as relevant vocal biomarkers capable of differentiating healthy subjects, early untreated PD patients and mid-advanced L-Dopa treated patients.

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“…[6] explored head and neck cancer. Interestingly, promising results were also highlighted for pathologies leading to vocal alterations only secondarily, such as neurodegenerative pathologies (Parkinson’s, SLA) [7] , [8] , Down syndrome [9] , and even cardiovascular disorders [10] , [11] , [12] as well as movement disorders [13] .…”

Section: Introductionmentioning

confidence: 99%