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Voice Analysis in Patients With Neurologic Diseases

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ClinicalTrials.gov Identifier: NCT04846413
Recruitment Status : Recruiting
First Posted : April 15, 2021
Last Update Posted : October 4, 2021
Sponsor:
Information provided by (Responsible Party):
Antonio Suppa, Neuromed IRCCS

Study Description
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Brief Summary:
In this observational pilot study, the investigators will record and assess voice samples from healthy participants and those participants affected by neurologic diseases to evaluate possible differences in voice features.

Condition or disease Intervention/treatment
Voice Disorders Neurologic Disorder Other: Speech task

Detailed Description:

In this study, the investigators will evaluate the clinical features of healthy participants and those participants with neurologic disorders by applying dedicated clinical scales. Also, the investigators will assess voice impairment by using perceptual examination tools. Then, the investigators will apply spectral analysis to assess the main frequency components of voice in healthy participants and in patients affected by neurologic disorders with a prominent voice impairment. To distinguish between healthy participants and patients affected by various neurologic diseases, the investigators will apply a voice analysis based on support vector machine (SVM) classifier that included a large number of features in addition to the main frequency components of voice.

For these purposes, the investigators will assess in detail the sensitivity, specificity, positive predictive value, and negative predictive value and accuracy of all diagnostic tests. Furthermore, the investigators will calculate the area under the receiver operating characteristic (ROC) curves to verify the optimal diagnostic threshold as reflected by the associated criterion (Ass. Crit.) and Youden Index (YI). To assess possible clinical-instrumental correlations, the investigators will also use a modified algorithm of SVM analysis to calculate a continuous numerical value (the likelihood ratio [LR]) providing a measure of voice impairment severity for each participant.

Voice recordings will be performed by asking participants to produce a specific speech task with their usual voice intensity, pitch, and quality. The speech task will consist of a sustained emission of a close mid-front unrounded vowel /e/ for at least 5 seconds. Voice recordings will be collected by using a high-definition audio-recorder placed at a distance of 5 cm from the mouth. Voice samples will be recorded in linear PCM format (.wav) at a sampling rate of 44.1 kHz, with 24-bit sample size. Voice analysis will consist of three separate processes: feature extraction, selection and classification. For feature extraction, the investigators will use the OpenSMILE (audEERING GmbH, Germany), dedicated software. Then, the investigators will select and classify voice feature by using SVM algorithm included in Weka.

Study Design
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Study Type : Observational
Estimated Enrollment : 100 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Advanced Voice Analysis With Machine Learning Algorithms in Patients With Neurologic Diseases
Actual Study Start Date : September 1, 2021
Estimated Primary Completion Date : July 31, 2022
Estimated Study Completion Date : July 31, 2023

Resource links provided by the National Library of Medicine


Groups and Cohorts
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Group/Cohort Intervention/treatment
Patients
Patients affected by neurologic disorders showing a prominent voice impairment.
 Other: Speech task
Speech task which consists of a sustained emission of the vowel /e/.


Outcome Measures
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Primary Outcome Measures :
  1. Voice analysis [ Time Frame: Voice analysis with machine learning algorithms will be implemented immediately after voice recording, during the clinical evaluation of each participant. ]
    Voice features obtained by using Support Vector Machine algorithm


Eligibility Criteria
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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Non-Probability Sample
Study Population
We will recruit neurologic patients not taking any oral medications or alcohol and any drugs acting on the central nervous system at the time of the study.
Criteria

Inclusion Criteria:

  • Clinical diagnosis of neurologic disorders

Exclusion Criteria:

  • smoking
  • bilateral/unilateral hearing loss
  • respiratory disorders
  • conditions affecting the vocal cords, including nodules.
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04846413


Contacts
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Contact: Antonio Suppa, MD, PhD 3494940365 ext +0039 antonio.suppa@uniroma1.it

Locations
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Italy
Antonio Suppa Recruiting
Pozzilli, Italy, 86077
Contact: Antonio Suppa    +00393494940365    antonio.suppa@uniroma1.it   
Sponsors and Collaborators
Neuromed IRCCS
More Information
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Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information
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Responsible Party: Antonio Suppa, Principal Investigator, Neuromed IRCCS
ClinicalTrials.gov Identifier: NCT04846413    
Other Study ID Numbers: DIPNEUROSCI_01
First Posted: April 15, 2021    Key Record Dates
Last Update Posted: October 4, 2021
Last Verified: September 2021

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Antonio Suppa, Neuromed IRCCS:
voice analysis
machine learning
neurologic disorders
Additional relevant MeSH terms:
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Voice Disorders
Nervous System Diseases
Disease
Pathologic Processes
 Laryngeal Diseases
Respiratory Tract Diseases
Otorhinolaryngologic Diseases
Neurologic Manifestations