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

NCT06160674 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 68

Last updated 2024-11-25

No results posted yet for this study

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

Interventions

OTHER

COPD

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.

Sponsors & Collaborators

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

    collaborator UNKNOWN

  • Blekinge Institute of Technology

    lead OTHER

Principal Investigators

  • Johan Sanmartin Berglund, MD, PhD · Blekinge Institute of Technology

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-11-28
Primary Completion
2024-10-30
Completion
2024-11-30

Countries

  • Sweden

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06160674 on ClinicalTrials.gov