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

NCT06160674 | Active Not Recruiting DMC

• 1 other identifier: BTH-6.1.1-0169-2023
• Study Type: observational
• Target: 68
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Chronic Obstructive Pulmonary Disease

Keywords

Segmentation; Classification; COPD; Machine Learning

Outcome Measures

Primary Outcomes (1)

• Classification performance

  Binary classification performance of the ML algorithm on each segment.

  30 weeks

Study Arms (2)

COPD

30 COPD participants, 16 Female and 14 Male.

Other: COPD

HC

38 HC participants, 20 Female and 18 Male.

Other: COPD

Interventions

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.

Also known as: HC

COPD; HC

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• being 18 years old and older.

You may not qualify if:

• being under 18 years old and older.

Sponsors & Collaborators

• Blekinge Institute of Technology: lead
• Excellence Center at Linköping - Lund in Information Technology (ELLIIT): collaborator

Study Sites (1)

Blekinge Institute of Technology

Karlskrona, Blekinge County, 37179, Sweden

Location

Related Publications (1)

• Idrisoglu A, Moraes ALD, Cheddad A, Anderberg P, Jakobsson A, Berglund JS. Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease. Sci Rep. 2025 Mar 22;15(1):9930. doi: 10.1038/s41598-025-95320-3.

  PMID: 40121302 DERIVED

MeSH Terms

Conditions

Pulmonary Disease, Chronic Obstructive

Condition Hierarchy (Ancestors)

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

Study Officials

• Johan Sanmartin Berglund, MD, PhD

  Blekinge Institute of Technology

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: CROSS SECTIONAL
• Target Duration: 6 Months
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 28, 2023
• First Posted: December 7, 2023
• Study Start: November 28, 2023
• Primary Completion: October 30, 2024
• Study Completion: November 30, 2024
• Last Updated: November 25, 2024

Record last verified: 2024-11

Data Sharing

• IPD Sharing: Will not share

Locations

SE (1)