Iterative envelope mean fractal dimension filter for the separation of crackles from normal breath sounds

Abstract

This paper presents a new method of separating pulmonary crackles from normal breath sounds: the iterative envelope mean fractal dimension (IEM-FD) filter. Crackles are an important physiological parameter for evaluating lung condition of an individual and their automatic separation from normal breath sounds can provide an objective way of diagnosing or monitoring different cardiopulmonary diseases. The filter combines the new iterative envelope mean (IEM) method with the established fractal dimension (FD) technique. The IEM method estimates the non-stationary and stationary parts of the lung sound signal and then the FD technique is applied to the estimated non-stationary output of the IEM method for further refining the separation process. The IEM-FD filter is tested using a publicly available dataset and, compared with an established crackle separation technique. The IEM-FD achieves high accuracy for crackle detection in the presence of noise with SNR >= -1 dB for fine crackles and SNR > +1 dB for coarse crackles, and has low computational cost, with minimal under- or over- estimation and good preservation of crackle morphology. The method is shown to have an overall performance suitable for automated analysis to determine accurately the number and characteristics of pulmonary crackles in a recorded lung sound.