Akademik Veri Yönetim Sistemi
Journal of Intelligent Systems with Applications, cilt.6, sa.2, ss.44-54, 2023 (Hakemli Dergi)
Respiratory diseases, both acute and chronic, are
widespread due to exposure to harmful substances in the environment, workplace, and through personal behaviors. Furthermore, the COVID-19 pandemic has led to both short-term
and long-term lung damage in survivors. Therefore, accurate
identification of chronic respiratory diseases, in particular, is
vital for effective management and treatment. Auscultation, the
practice of listening to respiratory sounds, plays a crucial role
in diagnosing respiratory diseases. By accurately interpreting
these sounds, complemented by other clinical findings, specialists
can make reliable diagnoses with minimal errors. However, the
effectiveness of auscultation is heavily influenced by the doctor’s
experience and environmental noise. To address these limitations,
automatic classification of respiratory sounds recorded with a
digital stethoscope using expert software has emerged as a
popular research area. This approach eliminates the reliance
on subjective interpretation by specialists. Unfortunately, as with
many biomedical signals, researchers face significant challenges.
The most pressing issue is the need for high-quality, accurately
labeled, and extensive lung and respiratory sound datasets.
Additionally, removing noise that distorts these sound signals
is another major obstacle. This brief review aims to delve into
these two primary challenges and provide examples of potential
solutions from relevant literature.