Voice analysis in adductor spasmodic dysphonia: Objective diagnosis and response to botulinum toxin. (April 2020)

• Record Type: Journal Article
• Authors: Suppa, Antonio; Asci, Francesco; Saggio, Giovanni; Marsili, Luca; Casali, Daniele; Zarezadeh, Zakarya; Ruoppolo, Giovanni; Berardelli, Alfredo; Costantini, Giovanni
• Is Part Of: Parkinsonism & related disorders. Volume 73(2020)
• Page Start: 23
• Page End: 30
• Publication Date: 2020-04
• Subjects: Adductor-type spasmodic dysphonia -- Voice analysis -- Cepstral analysis -- Machine-learning -- Botulinum toxin; Parkinson's disease -- Periodicals; Movement disorders -- Periodicals; Movement Disorders -- Periodicals; Nerve Degeneration -- Periodicals; Nervous System Diseases -- Periodicals; Parkinson Disease -- Periodicals; Tremor -- Periodicals; Parkinson, Maladie de -- Périodiques; Parkinson's disease; 616.833
• DOI: 10.1016/j.parkreldis.2020.03.012
• Languages: English
• ISSNs: 1353-8020

Abstract

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. Highlights: We propose to assess ASD patients by applying objective and quantitative tools. Cepstral analysis quantifies voices of ASD patients before and after BoNT-A therapy. Machine-learning discriminates between HS and ASD patients before and after BoNT-A. Cepstral analysis and machine-learning may improve ASD diagnosis and follow-up. Voice analysis parameters correlate with the severity of voice impairment.