An Improved Empirical Mode Decomposition Algorithm for Phonocardiogram Signal De-noising and Its Application in S1/S2 Extraction_Journal of Biomedical Engineering

Authors：

GONGJing ,  NIEShengdong , WANGYuanjun

School of Medical Instrument & Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;

Corresponding author：

NIEShengdong, Email: nsd4647@163.com

Keywords：

empirical mode decomposition; wavelet-transform; energy envelope; phonocardiogram

DOI：

10.7507/1001-5515.20150173

Video：

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Abstract Full text Figures/Tables Video References Cited by

In this paper, an improved empirical mode decomposition (EMD) algorithm for phonocardiogram (PCG) signal de-noising is proposed. Based on PCG signal processing theory, the S1/S2 components can be extracted by combining the improved EMD-Wavelet algorithm and Shannon energy envelope algorithm. Firstly, by applying EMD-Wavelet algorithm for pre-processing, the PCG signal was well filtered. Then, the filtered PCG signal was saved and applied in the following processing steps. Secondly, time domain features, frequency domain features and energy envelope of the each intrinsic mode function's (IMF) were computed. Based on the time frequency domain features of PCG's IMF components which were extracted from the EMD algorithm and energy envelope of the PCG, the S1/S2 components were pinpointed accurately. Meanwhile, a detecting fixed method, which was based on the time domain processing, was proposed to amend the detection results. Finally, to test the performance of the algorithm proposed in this paper, a series of experiments was contrived. The experiments with thirty samples were tested for validating the effectiveness of the new method. Results of test experiments revealed that the accuracy for recognizing S1/S2 components was as high as 99.75%. Comparing the results of the method proposed in this paper with those of traditional algorithm, the detection accuracy was increased by 5.56%. The detection results showed that the algorithm described in this paper was effective and accurate. The work described in this paper will be utilized in the further studying on identity recognition.

Citation： GONGJing, NIEShengdong, WANGYuanjun. An Improved Empirical Mode Decomposition Algorithm for Phonocardiogram Signal De-noising and Its Application in S1/S2 Extraction. Journal of Biomedical Engineering, 2015, 32(5): 970-974. doi: 10.7507/1001-5515.20150173 Copy

1.	MEZIANI F, DEBBAL S M, ATBI A. Analysis of phonocardiogram signals using wavelet transform[J]. J Med Eng Technol, 2012, 36(6):283-302.
2.	HAMZA CHERIF L, DEBBAL S M. Algorithm for detection of the internal components of the heart sounds and their split using a Hilbert transform[J]. J Med Eng Technol, 2013, 37(3):220-230.
3.	JABBARI S, GHASSEMIAN H. Modeling of heart systolic murmurs based on multivariate matching pursuit for diagnosis of valvular disorders[J]. Comput Biol Med, 2011, 41(9):802-811.
4.	SANEI S, GHODSI M, HASSANI H. An adaptive singular spectrum analysis approach to murmur detection from heart sounds[J]. Med Eng Phys, 2011, 33(3):362-367.
5.	EL-SEGAIER M, LILJA O, LUKKARINEN S, et al. Computer-based detection and analysis of heart sound and murmur[J]. Ann Biomed Eng, 2005, 33(7):937-942.
6.	NASERI H, HOMAEINEZHAD M R. Detection and boundary identification of phonocardiogram sounds using an expert frequency-energy based metric[J]. Ann Biomed Eng, 2013, 41(2):279-292.
7.	CHENG Xiefeng, MA Yong, LIU Chen, et al. Research on heart sound identification technology[J]. Sci China Inf Sci, 2012, 55(2):281-292.
8.	WANG P, KIM Y, LING L H, et al. First heart sound detection for phonocardiogram segmentation[C]//27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. Shanghai:2006:5519-5522.
9.	SAFARA F, DORAISAMY S, AZMAN A, et al. Wavelet packet entropy for heart murmurs classification[J]. Adv Bioinformatics, 2012, 2012:327269.
10.	LI Nianqiang, LI Ping. An improved algorithm based on EMD-wavelet for ECG signal de-noising[J]. 2009 International Joint Conference on Computational Sciences and Optimization. Sanya:2009, 1:825-827.
11.	ZIA M K, GRIFFEL B, FRIDMAN V, et al. Noise detection in heart sound recordings[C]//2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC. Boston, MA:2011:5880-5883.
12.	BAI J, SILESHI G, NORDEHN G, et al. Development of laser-based heart sound detection system[J]. J Biomedical Science and Engineering, 2012, 5(1):34-37.
13.	AKBARI M A, HASSANI K, DOYLE J D, et al. Digital Subtraction Phonocardiography (DSP) applied to the detection and characterization of heart murmurs[J]. Biomed Eng Online, 2011, 10:109.
14.	KABIR M A, SHAHNAZ C. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains[J]. Biomed Signal Process Control, 2012, 7(5):481-489.
15.	ERGEN B, TATAR Y, GULCUR H O. Time-frequency analysis of phonocardiogram signals using wavelet transform:a comparative study[J]. Comput Methods Biomech Biomed Engin, 2012, 15(4):371-381.
16.	CHERIF L H, DEBBAL S M, BEREKSI-REGUIG F. Choice of the wavelet analyzing in the phonocardiogram signal analysis using the discrete and the packet wavelet transform[J]. Expert Syst Appl, 2010, 37(2):913-918.
17.	NIGAM V, PRIEMER R. A simplicity-based fuzzy clustering approach for detection and extraction of murmurs from the phonocardiogram[J]. Physiol Meas, 2008, 29(1):33-47.
18.	DE PANFILIS S, MORONI C, PECCIANTI M, et al. Multi-point accelerometric detection and principal component analysis of heart sounds[J]. Physiol Meas, 2013, 34(3):L1-L9.

1. MEZIANI F, DEBBAL S M, ATBI A. Analysis of phonocardiogram signals using wavelet transform[J]. J Med Eng Technol, 2012, 36(6):283-302.
2. HAMZA CHERIF L, DEBBAL S M. Algorithm for detection of the internal components of the heart sounds and their split using a Hilbert transform[J]. J Med Eng Technol, 2013, 37(3):220-230.
3. JABBARI S, GHASSEMIAN H. Modeling of heart systolic murmurs based on multivariate matching pursuit for diagnosis of valvular disorders[J]. Comput Biol Med, 2011, 41(9):802-811.
4. SANEI S, GHODSI M, HASSANI H. An adaptive singular spectrum analysis approach to murmur detection from heart sounds[J]. Med Eng Phys, 2011, 33(3):362-367.
5. EL-SEGAIER M, LILJA O, LUKKARINEN S, et al. Computer-based detection and analysis of heart sound and murmur[J]. Ann Biomed Eng, 2005, 33(7):937-942.
6. NASERI H, HOMAEINEZHAD M R. Detection and boundary identification of phonocardiogram sounds using an expert frequency-energy based metric[J]. Ann Biomed Eng, 2013, 41(2):279-292.
7. CHENG Xiefeng, MA Yong, LIU Chen, et al. Research on heart sound identification technology[J]. Sci China Inf Sci, 2012, 55(2):281-292.
8. WANG P, KIM Y, LING L H, et al. First heart sound detection for phonocardiogram segmentation[C]//27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. Shanghai:2006:5519-5522.
9. SAFARA F, DORAISAMY S, AZMAN A, et al. Wavelet packet entropy for heart murmurs classification[J]. Adv Bioinformatics, 2012, 2012:327269.
10. LI Nianqiang, LI Ping. An improved algorithm based on EMD-wavelet for ECG signal de-noising[J]. 2009 International Joint Conference on Computational Sciences and Optimization. Sanya:2009, 1:825-827.
11. ZIA M K, GRIFFEL B, FRIDMAN V, et al. Noise detection in heart sound recordings[C]//2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC. Boston, MA:2011:5880-5883.
12. BAI J, SILESHI G, NORDEHN G, et al. Development of laser-based heart sound detection system[J]. J Biomedical Science and Engineering, 2012, 5(1):34-37.
13. AKBARI M A, HASSANI K, DOYLE J D, et al. Digital Subtraction Phonocardiography (DSP) applied to the detection and characterization of heart murmurs[J]. Biomed Eng Online, 2011, 10:109.
14. KABIR M A, SHAHNAZ C. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains[J]. Biomed Signal Process Control, 2012, 7(5):481-489.
15. ERGEN B, TATAR Y, GULCUR H O. Time-frequency analysis of phonocardiogram signals using wavelet transform:a comparative study[J]. Comput Methods Biomech Biomed Engin, 2012, 15(4):371-381.
16. CHERIF L H, DEBBAL S M, BEREKSI-REGUIG F. Choice of the wavelet analyzing in the phonocardiogram signal analysis using the discrete and the packet wavelet transform[J]. Expert Syst Appl, 2010, 37(2):913-918.
17. NIGAM V, PRIEMER R. A simplicity-based fuzzy clustering approach for detection and extraction of murmurs from the phonocardiogram[J]. Physiol Meas, 2008, 29(1):33-47.
18. DE PANFILIS S, MORONI C, PECCIANTI M, et al. Multi-point accelerometric detection and principal component analysis of heart sounds[J]. Physiol Meas, 2013, 34(3):L1-L9.

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An Improved Empirical Mode Decomposition Algorithm for Phonocardiogram Signal De-noising and Its Application in S1/S2 Extraction

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