A robust method for heart sounds localization using lung sounds entropy

• Published in: IEEE transactions on biomedical engineering Vol. 53; no. 3; pp. 497 - 502
• Main Authors: Yadollahi, A., Moussavi, Z.M.K.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2006; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustics; Adult; Cancellation; Diagnosis, Computer-Assisted - methods; Energy resolution; Entropy; Female; Flow rate; Frequency; Heart; Heart - physiology; Heart Sounds - physiology; heart sounds localization; Humans; Interference; Lung - physiology; lung sounds; Lungs; Male; multiresolution product; Noise reduction; Reproducibility of Results; Respiratory Sounds - physiology; Robustness; Segments; Sensitivity and Specificity; Signal resolution; Sound localization; Sound Spectrography - methods; wavelet; Wavelet coefficients
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2005.869789

Heart sounds are the main unavoidable interference in lung sound recording and analysis. Hence, several techniques have been developed to reduce or cancel heart sounds (HS) from lung sound records. The first step in most HS cancellation techniques is to detect the segments including HS. This paper proposes a novel method for HS localization using entropy of the lung sounds. We investigated both Shannon and Renyi entropies and the results of the method using Shannon entropy were superior. Another HS localization method based on multiresolution product of lung sounds wavelet coefficients adopted from was also implemented for comparison. The methods were tested on data from 6 healthy subjects recorded at low (7.5 ml/s/kg) and medium (15 ml/s/kg) flow rates. The error of entropy-based method using Shannon entropy was found to be 0.1 /spl plusmn/ 0.4% and 1.0 /spl plusmn/ 0.7% at low and medium flow rates, respectively, which is significantly lower than that of multiresolution product method and those of other methods reported in previous studies. The proposed method is fully automated and detects HS included segments in a completely unsupervised manner.