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OpenTrials
Active, Not Recruiting

NCT Number: NCT06160674

Vowel Segmentation for Classification of Chronic Obstructive Pulmonary Disease Using Machine Learning

This work aims to evaluate whether the segmentation of vowel recordings collected from patients diagnosed with COPD and healthy control groups can increase the classification precision of machine learning techniques.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Chronic Obstructive Pulmonary Disease Chronic Disease Disease Attributes Lung Diseases Lung Diseases, Obstructive Pathologic Processes Pathological Conditions, Signs and Symptoms Pulmonary Disease, Chronic Obstructive Respiratory Tract Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Blekinge Institute of Technology

Karlskrona, Blekinge County, 37179, Sweden

About this study

Voice data and sociodemographic data on gender and age will be collected through the "VoiceDiganostic" application from the company Voice Diagnostic. Collected vowel recordings will be segmented and tested to determine whether some segments contain more information for the discrimination of COPD from healthy control groups.

Each segment will be transformed into mathematical vocal measures called voice features. A dataset consisting of voice features in conjunction with demographics and health data will be constructed for each segment which in turn will be evaluated for classification performance using several machine learning algorithms.

Descriptive statistical analysis will be held on attributes containing information on input data and gained outcomes from ML algorithms. The achieved results will be presented in the form of summary tables and graphs.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • being 18 years old and older.

Exclusion criteria

  • being under 18 years old and older.

Treatment and study plan

COPD

Other

A vowel segmentation data set consisting of information from COPD and HC groups will be used to experiment with the classification performance of several Machine Learning techniques on different segments of a vowel recording.

Other names: HC

Primary outcomes

  1. Classification performance

    Time frame: 30 weeks

    Binary classification performance of the ML algorithm on each segment.

Sponsors and collaborators

Lead sponsor

Blekinge Institute of Technology

Other

Collaborators

  • Excellence Center at Linköping - Lund in Information Technology (ELLIIT)

Registry information

Important dates

Study start
2023
Primary completion
2024
Study completion
2024
First posted
Dec 7, 2023
Registry last updated
Nov 25, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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