Creating and Assessing a Voice Dataset for Automated Classification of Chronic Obstructive Pulmonary Disease

March 17, 2025 updated by: Johan Sanmartin Berglund, Blekinge Institute of Technology
This work aims to evaluate whether voice recordings collected from patients diagnosed with COPD and healthy control groups can be used to detect the disease using machine learning techniques.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

Voice data and sociodemographic data on gender and age will be collected through the "VoiceDiganostic" application from the company Voice Diagnostic, which allows one to participate without location dependency. Participants with a diagnosis will be marked as the COPD group, and others will be marked as the healthy control group. Private information such as known comorbidities, personal security numbers, health parameters and communication information will be separately noticed in a participation table for each group.

The collected data 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 further usage as an input to ML techniques.

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.

Study Type

Observational

Enrollment (Actual)

72

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Sweden
    • Blekinge
      • Karlskrona, Blekinge, Sweden, 37179
        • Blekinge Institute of Technology

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

Sampling Method

Non-Probability Sample

Study Population

Data will be collected from participants 18 years old and older with and without COPD diagnosis will be recruited.

Description

Inclusion Criteria:

  • being 18 years old and older.

Exclusion Criteria:

  • being under 18 years old.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
COPD
Participants with clinically diagnosed Chronic obstructive pulmonary disease. Total 34 recruitment, 18 Female, 16 Male
Other: COPD
A data set consisting of information from COPD and HC groups will be used to experiment with the classification performance of several Machine Learning techniques.
Other Names:
  • HC
HC
Participants without Chronic obstructive pulmonary disease diagnosis. Total 38 recruitment, 20 Female, 18 Male
Other: COPD
A data set consisting of information from COPD and HC groups will be used to experiment with the classification performance of several Machine Learning techniques.
Other Names:
  • HC

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy
Time Frame: Week 51
Binary detection performance of the ML algorithm
Week 51
Input data importance scale
Time Frame: Week 51
Features used as input data will be ranked from most important to less important one.
Week 51

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Blekinge Institute of Technology

Collaborators

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

Investigators

  • Principal Investigator: Johan Sanmartin Berglund, MD, PhD, Blekinge Institute of Technology

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 16, 2021

Primary Completion (Actual)

August 12, 2024

Study Completion (Actual)

October 30, 2024

Study Registration Dates

First Submitted

June 1, 2023

First Submitted That Met QC Criteria

June 1, 2023

First Posted (Actual)

June 9, 2023

Study Record Updates

Last Update Posted (Actual)

March 25, 2025

Last Update Submitted That Met QC Criteria

March 17, 2025

Last Verified

March 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • BTH-6.1.1-0074-2023

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Participant data can not be shared due to the GDPR. However, the dataset created can be available upon request from the institution.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

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.

Subscribe