Vocal test Analysis for Assessing Parkinson's Disease at Early Stage

Fayad, Rayan; Hajj-Hassan, Mohamad; Constantini, Giovanni; Zarazadeh, Zakarya; Errico, Vito; Pisani, Antonio; Lazzaro, Giulia Di; Ricci, Mariachiara; Saggio, Giovanni

doi:10.1109/icabme53305.2021.9604891

Cited by 3 publications

(2 citation statements)

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“…PDP usually face several motor and non-motor symptoms ( 4 – 6 ), including tremors at rest, bradykinesia, rigidity, dyskynesia ( 7 ), gait impairment ( 8 ), postural instability, sleep alterations, and voice disorders. Among these, it was estimated that vocal dysfunctions, usually grouped under the definition of dysarthria and dysphonia ( 9 – 11 ), affect ~90% of the PDP population. The incidence of these alterations, as well as the feasibility of rapid, non-invasive, and inexpensive signal collection, have made voice signal analysis a powerful and effective tool in supporting clinical practice ( 11 , 12 ).…”

Section: Introductionmentioning

confidence: 99%

“…Among these, it was estimated that vocal dysfunctions, usually grouped under the definition of dysarthria and dysphonia ( 9 – 11 ), affect ~90% of the PDP population. The incidence of these alterations, as well as the feasibility of rapid, non-invasive, and inexpensive signal collection, have made voice signal analysis a powerful and effective tool in supporting clinical practice ( 11 , 12 ).…”

Section: Introductionmentioning

confidence: 99%

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Robust and language-independent acoustic features in Parkinson's disease

Scimeca,

Amato,

Olmo

et al. 2023

Front. Neurol.

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IntroductionThe analysis of vocal samples from patients with Parkinson's disease (PDP) can be relevant in supporting early diagnosis and disease monitoring. Intriguingly, speech analysis embeds several complexities influenced by speaker characteristics (e.g., gender and language) and recording conditions (e.g., professional microphones or smartphones, supervised, or non-supervised data collection). Moreover, the set of vocal tasks performed, such as sustained phonation, reading text, or monologue, strongly affects the speech dimension investigated, the feature extracted, and, as a consequence, the performance of the overall algorithm.MethodsWe employed six datasets, including a cohort of 176 Healthy Control (HC) participants and 178 PDP from different nationalities (i.e., Italian, Spanish, Czech), recorded in variable scenarios through various devices (i.e., professional microphones and smartphones), and performing several speech exercises (i.e., vowel phonation, sentence repetition). Aiming to identify the effectiveness of different vocal tasks and the trustworthiness of features independent of external co-factors such as language, gender, and data collection modality, we performed several intra- and inter-corpora statistical analyses. In addition, we compared the performance of different feature selection and classification models to evaluate the most robust and performing pipeline.ResultsAccording to our results, the combined use of sustained phonation and sentence repetition should be preferred over a single exercise. As for the set of features, the Mel Frequency Cepstral Coefficients demonstrated to be among the most effective parameters in discriminating between HC and PDP, also in the presence of heterogeneous languages and acquisition techniques.ConclusionEven though preliminary, the results of this work can be exploited to define a speech protocol that can effectively capture vocal alterations while minimizing the effort required to the patient. Moreover, the statistical analysis identified a set of features minimally dependent on gender, language, and recording modalities. This discloses the feasibility of extensive cross-corpora tests to develop robust and reliable tools for disease monitoring and staging and PDP follow-up.

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