Clinical trial NCT04846413: Voice Analysis in Patients With Neurologic Diseases
Official title: Advanced Voice Analysis With Machine Learning Algorithms in Patients With Neurologic Diseases
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Voice Disorders
Neurologic Disorder
Keywords: voice analysis
machine learning
neurologic disorders
Lead sponsor: Neuromed IRCCS
Sponsor class: OTHER
First posted: 2021-04-15
Enrollment: 100
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.

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.

Eligibility criteria: Inclusion Criteria:

* Clinical diagnosis of neurologic disorders

Exclusion Criteria:

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

Primary outcomes:
measure: Voice analysis
timeFrame: Voice analysis with machine learning algorithms will be implemented immediately after voice recording, during the clinical evaluation of each participant.
Secondary outcomes:
Other outcomes:
Locations:
city: Pozzilli
country: Italy
facility: Antonio Suppa
Study officials:
References: