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Status unknownNCT04846413Updated Mar 22, 2023

Voice Analysis in Patients With Neurologic Diseases

An observational study in Voice Disorders and Neurologic Disorder, sponsored by Neuromed IRCCS. Status unknown at 1 site in Italy. Per ClinicalTrials.gov, last updated 2023-03-22.

Sponsored by Neuromed IRCCS · Observational

The sponsor has not verified this record recently (last verified Mar 2023), so the status shown — last known as Recruiting — may be out of date.
Study type
Observational
Model
Cohort
Time perspective
Prospective
Enrollment
100
Sex
All
01

Study 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.

Read the 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.

02

Conditions studied

  • Voice Disorders
  • Neurologic Disorder

Keywords

  • voice analysis
  • machine learning
  • neurologic disorders
03

Who can participate

Ages eligible
Child (0–17), Adult (18–64), Older adult (65+)
Sexes eligible
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.

Inclusion criteria

  • Clinical diagnosis of neurologic disorders

Exclusion criteria

Exclusion Criteria:

  • smoking
  • bilateral/unilateral hearing loss
  • respiratory disorders
  • conditions affecting the vocal cords, including nodules.
04

Study design

Observational model
Cohort
Time perspective
Prospective
Enrollment
100 participants (estimated)
Patient registry
No

Groups and cohorts

  • Patients

    Patients affected by neurologic disorders showing a prominent voice impairment.

    Other: Speech task

Interventions

  • OtherSpeech task

    Speech task which consists of a sustained emission of the vowel /e/.

05

What researchers measure

Primary outcomes

  1. Voice analysis

    Voice features obtained by using Support Vector Machine algorithm

    Time frame: Voice analysis with machine learning algorithms will be implemented immediately after voice recording, during the clinical evaluation of each participant.

06

Study locations

1 of 1 sites recruiting
07

Registry details

Key details

Study ID
NCT04846413
Lead sponsor
Neuromed IRCCS
Responsible party
Antonio Suppa (Principal Investigator, Neuromed IRCCS) — Principal investigator
First posted
Apr 15, 2021
Start date
Sep 1, 2021
Primary completion
Jul 31, 2022
Completion
Jul 31, 2023 (estimated)
Last update
Mar 22, 2023

Study contacts

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

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is status unknown, as verified in Mar 2023. You cannot join it, but the record below documents what was studied.

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