Voice Analysis in Patients With Neurologic Diseases
Advanced Voice Analysis With Machine Learning Algorithms in Patients With Neurologic Diseases
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
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 Type
Study Type
Enrollment (Anticipated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Antonio Suppa, MD, PhD
- Phone Number: +0039 3494940365
- Email: antonio.suppa@uniroma1.it
Study Locations
-
-
-
Pozzilli, Italy, 86077
- Recruiting
- Antonio Suppa
-
Contact:
- Antonio Suppa
- Phone Number: +00393494940365
- Email: antonio.suppa@uniroma1.it
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- Clinical diagnosis of neurologic disorders
Exclusion Criteria:
- smoking
- bilateral/unilateral hearing loss
- respiratory disorders
- conditions affecting the vocal cords, including nodules.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Patients
Patients affected by neurologic disorders showing a prominent voice impairment.
|
Speech task which consists of a sustained emission of the vowel /e/.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
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
|
Voice analysis with machine learning algorithms will be implemented immediately after voice recording, during the clinical evaluation of each participant.
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Anticipated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- DIPNEUROSCI_01
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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