BIOMEDICAL SIGNAL PROCESSING AND CONTROL, vol.38, pp.322-336, 2017 (Journal Indexed in SCI)
Article / Article
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Science Citation Index Expanded (SCI-EXPANDED), Scopus
Pulmonary sound, Rational dilation wavelet transform, Q-factor, Ensemble learning, Adventitious lung sounds, Statistical feature extraction, LUNG SOUNDS, NEURAL-NETWORK, CLASSIFICATION, FREQUENCY, CRACKLES
Yıldız Technical University Affiliated:
Background and objective: Crackle, wheeze and normal lung sound discrimination is vital in diagnosing pulmonary diseases. Previous works suffer from limited frequency resolution and lack of the ability to deal with oscillatory signals (wheezes). The main objective of this study is to propose a novel wavelet based lung sound classification system that is capable of adaptively representing crackle, wheeze and normal lung sound signal time-frequency properties.