Antonio Suppa1, Francesco Asci2, Giovanni Saggio3, Luca Marsili4, Daniele Casali3, Zakarya Zarezadeh5, Giovanni Ruoppolo6, Alfredo Berardelli7, Giovanni Costantini3. 1. Department of Human Neurosciences, Sapienza University of Rome, Italy; IRCCS Neuromed Institute, Pozzilli, IS, Italy. 2. Department of Human Neurosciences, Sapienza University of Rome, Italy. 3. Department of Electronic Engineering, University of Rome Tor Vergata, Italy. 4. Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, OH, USA. 5. Department of History, Humanities and Society, University of Rome Tor Vergata, Italy. 6. Department of Sense Organs, Otorhinolaryngology Section, Sapienza University of Rome, Italy. 7. Department of Human Neurosciences, Sapienza University of Rome, Italy; IRCCS Neuromed Institute, Pozzilli, IS, Italy. Electronic address: alfredo.berardelli@uniroma1.it.
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.
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.
Authors: Carlo Robotti; Giovanni Costantini; Giovanni Saggio; Valerio Cesarini; Anna Calastri; Eugenia Maiorano; Davide Piloni; Tiziano Perrone; Umberto Sabatini; Virginia Valeria Ferretti; Irene Cassaniti; Fausto Baldanti; Andrea Gravina; Ahmed Sakib; Elena Alessi; Matteo Pascucci; Daniele Casali; Zakarya Zarezadeh; Vincenzo Del Zoppo; Antonio Pisani; Marco Benazzo Journal: J Voice Date: 2021-11-26 Impact factor: 2.009
Authors: Antonio Suppa; Giovanni Costantini; Francesco Asci; Pietro Di Leo; Mohammad Sami Al-Wardat; Giulia Di Lazzaro; Simona Scalise; Antonio Pisani; Giovanni Saggio Journal: Front Neurol Date: 2022-02-15 Impact factor: 4.003
Authors: Robert Chen; Alfredo Berardelli; Amitabh Bhattacharya; Matteo Bologna; Kai-Hsiang Stanley Chen; Alfonso Fasano; Rick C Helmich; William D Hutchison; Nitish Kamble; Andrea A Kühn; Antonella Macerollo; Wolf-Julian Neumann; Pramod Kumar Pal; Giulia Paparella; Antonio Suppa; Kaviraja Udupa Journal: Clin Neurophysiol Pract Date: 2022-06-30
Authors: Giovanni Costantini; Valerio Cesarini Dr; Carlo Robotti; Marco Benazzo; Filomena Pietrantonio; Stefano Di Girolamo; Antonio Pisani; Pietro Canzi; Simone Mauramati; Giulia Bertino; Irene Cassaniti; Fausto Baldanti; Giovanni Saggio Journal: Knowl Based Syst Date: 2022-07-28 Impact factor: 8.139
Authors: Francesco Asci; Giovanni Costantini; Pietro Di Leo; Alessandro Zampogna; Giovanni Ruoppolo; Alfredo Berardelli; Giovanni Saggio; Antonio Suppa Journal: Sensors (Basel) Date: 2020-09-04 Impact factor: 3.576