Introduction: Adductor-type spasmodic dysphonia is a task-specific focal dystonia characterized by involuntary laryngeal muscle spasms. Due to the lack of quantitative instrumental tools, voice assessment in patients with adductor-type spasmodic dysphonia is mainly based on qualitative neurologic examination. We evaluated patients with cepstral analysis and specific machine-learning algorithms and compared the results with those collected in healthy subjects. In patients, we also used cepstral analysis and machine-learning algorithms to investigate the effect of botulinum neurotoxin type A. Methods: We investigated 60 patients affected by adductor-type spasmodic dysphonia before botulinum neurotoxin type A therapy and 60 age and gender-matched healthy subjects. A subgroup of 35 patients was also evaluated after botulinum neurotoxin type A therapy. We recorded the sustained emission of a vowel and a sentence by means of a high-definition audio recorder. Voice samples underwent cepstral analysis as well as machine-learning algorithm classification techniques. Results: Cepstral analysis was able to differentiate between healthy subjects and patients, but receiver operating characteristic curve analysis demonstrated that machine-learning algorithms achieved better results than cepstral analysis in differentiating healthy subjects and patients affected by adductor-type spasmodic dysphonia. Similar results were obtained when differentiating patients before and after botulinum neurotoxin type A therapy. Cepstral analysis and machine-learning measures correlated with the severity of voice impairment in patients before and after botulinum neurotoxin type A therapy. Conclusions: Cepstral analysis and machine-learning algorithms are new tools that offer meaningful support to clinicians in the diagnosis and treatment of adductor-type spasmodic dysphonia.

Voice analysis in adductor spasmodic dysphonia: Objective diagnosis and response to botulinum toxin / Suppa, A.; Asci, F.; Saggio, G.; Marsili, L.; Casali, D.; Zarezadeh, Z.; Ruoppolo, G.; Berardelli, A.; Costantini, G.. - In: PARKINSONISM & RELATED DISORDERS. - ISSN 1353-8020. - 73:(2020), pp. 23-30. [10.1016/j.parkreldis.2020.03.012]

Voice analysis in adductor spasmodic dysphonia: Objective diagnosis and response to botulinum toxin

Suppa A.
Co-primo
Conceptualization
;
Asci F.
Co-primo
Writing – Original Draft Preparation
;
Ruoppolo G.
Writing – Review & Editing
;
Berardelli A.
Penultimo
Supervision
;
2020

Abstract

Introduction: Adductor-type spasmodic dysphonia is a task-specific focal dystonia characterized by involuntary laryngeal muscle spasms. Due to the lack of quantitative instrumental tools, voice assessment in patients with adductor-type spasmodic dysphonia is mainly based on qualitative neurologic examination. We evaluated patients with cepstral analysis and specific machine-learning algorithms and compared the results with those collected in healthy subjects. In patients, we also used cepstral analysis and machine-learning algorithms to investigate the effect of botulinum neurotoxin type A. Methods: We investigated 60 patients affected by adductor-type spasmodic dysphonia before botulinum neurotoxin type A therapy and 60 age and gender-matched healthy subjects. A subgroup of 35 patients was also evaluated after botulinum neurotoxin type A therapy. We recorded the sustained emission of a vowel and a sentence by means of a high-definition audio recorder. Voice samples underwent cepstral analysis as well as machine-learning algorithm classification techniques. Results: Cepstral analysis was able to differentiate between healthy subjects and patients, but receiver operating characteristic curve analysis demonstrated that machine-learning algorithms achieved better results than cepstral analysis in differentiating healthy subjects and patients affected by adductor-type spasmodic dysphonia. Similar results were obtained when differentiating patients before and after botulinum neurotoxin type A therapy. Cepstral analysis and machine-learning measures correlated with the severity of voice impairment in patients before and after botulinum neurotoxin type A therapy. Conclusions: Cepstral analysis and machine-learning algorithms are new tools that offer meaningful support to clinicians in the diagnosis and treatment of adductor-type spasmodic dysphonia.
2020
Adductor-type spasmodic dysphonia; Botulinum toxin; Cepstral analysis; Machine-learning; Voice analysis
01 Pubblicazione su rivista::01a Articolo in rivista
Voice analysis in adductor spasmodic dysphonia: Objective diagnosis and response to botulinum toxin / Suppa, A.; Asci, F.; Saggio, G.; Marsili, L.; Casali, D.; Zarezadeh, Z.; Ruoppolo, G.; Berardelli, A.; Costantini, G.. - In: PARKINSONISM & RELATED DISORDERS. - ISSN 1353-8020. - 73:(2020), pp. 23-30. [10.1016/j.parkreldis.2020.03.012]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1401731
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