A pipelined recurrent fuzzy model for real-time analysis of lung sounds

This paper presents a recurrent fuzzy-neural filter that performs the task of separation of lung sounds, obtained from patients with pulmonary pathology. The filter is a pipelined Takagi–Sugeno–Kang recurrent fuzzy network, consisting of a number of modules interconnected in a cascaded form. The participating modules are implemented through recurrent fuzzy neural networks with internal dynamics. The structure of the modules is evolved sequentially from input–output data. Extensive experimental results, regarding the lung sound category of crackles, are given, and a performance comparison with a series of other fuzzy and neural filters is conducted, underlining the separation capabilities of the proposed filter.