Overcomplete discrete wavelet transform based respiratory sound discrimination with feature and decision level fusion

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.