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| Algorithm of Crackle Detection Based on TwoDimensional Texture Features by Fractional Hilbert Transform |
| School of Biological Science and Engineering, South China University of Technology, Guangzhou 510006, China |
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Abstract In order to detect an important kind of abnormal and discontinuous respiratory sounds ——crackles in lung sounds, a new detection algorithm has been proposed based on an emerging theory of fractional Hilbert transform. By applying fractional Hilbert transform to lung sound signals, a twodimension image with texture feature can be generated. The features of combination of dark and bright interlaced strips in texture images were corresponding to features of crackle signals in lung sounds, which can be employed to detect crackles. Two groups of detection experiments were carried out. First, real normal lung sounds signals with simulated crackles embedded were tested, and 100% crackles were detected. Then, lung sounds of patients with pulmonary disease were tested, and the sensitivity of detection accuracy reached to 97%. Thus, the effectiveness of crackle detection based on fractional Hilbert transform has been validated.
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