BACKGROUND: Patients with essential tremor have upper limb postural and action tremor often associated with voice tremor. The objective of this study was to objectively examine voice tremor and its response to symptomatic pharmacological treatment in patients with essential tremor using voice analysis consisting of power spectral analysis and machine learning. METHODS: We investigated 58 patients (24 men; mean age ± SD, 71.7 ± 9.2 years; range, 38-85 years) and 74 age- and sex-matched healthy subjects (20 men; mean age ± SD, 71.0 ± 12.4 years; range, 43-95 years). We recorded voice samples during sustained vowel emission using a high-definition audio recorder. Voice samples underwent sound signal analysis, including power spectral analysis and support vector machine classification. We compared voice recordings in patients with essential tremor who did and did not manifest clinically overt voice tremor and in patients who were and were not under the symptomatic effect of the best medical treatment. RESULTS: Power spectral analysis demonstrated a prominent oscillatory activity peak at 2-6 Hz in patients who manifested a clinically overt voice tremor. Voice analysis with support vector machine classifier objectively discriminated with high accuracy between controls and patients who did and did not manifest clinically overt voice tremor and between patients who were and were not under the symptomatic effect of the best medical treatment. CONCLUSIONS: In patients with essential tremor, voice tremor is characterized by abnormal oscillatory activity at 2-6 Hz. Voice analysis, including power spectral analysis and support vector machine classification, objectively detected voice tremor and its response to symptomatic pharmacological treatment in patients with essential tremor.
BACKGROUND:Patients with essential tremor have upper limb postural and action tremor often associated with voice tremor. The objective of this study was to objectively examine voice tremor and its response to symptomatic pharmacological treatment in patients with essential tremor using voice analysis consisting of power spectral analysis and machine learning. METHODS: We investigated 58 patients (24 men; mean age ± SD, 71.7 ± 9.2 years; range, 38-85 years) and 74 age- and sex-matched healthy subjects (20 men; mean age ± SD, 71.0 ± 12.4 years; range, 43-95 years). We recorded voice samples during sustained vowel emission using a high-definition audio recorder. Voice samples underwent sound signal analysis, including power spectral analysis and support vector machine classification. We compared voice recordings in patients with essential tremor who did and did not manifest clinically overt voice tremor and in patients who were and were not under the symptomatic effect of the best medical treatment. RESULTS: Power spectral analysis demonstrated a prominent oscillatory activity peak at 2-6 Hz in patients who manifested a clinically overt voice tremor. Voice analysis with support vector machine classifier objectively discriminated with high accuracy between controls and patients who did and did not manifest clinically overt voice tremor and between patients who were and were not under the symptomatic effect of the best medical treatment. CONCLUSIONS: In patients with essential tremor, voice tremor is characterized by abnormal oscillatory activity at 2-6 Hz. Voice analysis, including power spectral analysis and support vector machine classification, objectively detected voice tremor and its response to symptomatic pharmacological treatment in patients with essential tremor.
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