© The Institution of Engineering and Technology
A robust multistage decision-based heart sound delineation (MDHSD) method is presented for automatically determining the boundaries and peaks of heart sounds (S1, S2, S3, and S4), systolic, and diastolic murmurs (early, mid, and late) and high-pitched sounds (HPSs) of the phonocardiogram (PCG) signal. The proposed MDHSD method consists of the Gaussian kernels based signal decomposition (GSDs) and multistage decision-based delineation (MDBD). The GSD algorithm first removes the low-frequency (LF) artefacts and then decomposes the filtered signal into two subsignals: the LF sound part (S1, S2, S3, and S4) and the high-frequency sound part (murmurs and HPSs). The MDBD algorithm consists of absolute envelope extraction, adaptive thresholding, and fiducial point determination. The accuracy and robustness of the proposed method is evaluated using various types of normal and pathological PCG signals. Results show that the method achieves an average sensitivity of 98.22%, positive predictivity of 97.46%, and overall accuracy of 95.78%. The method yields maximum average delineation errors of 4.52 and 4.14 ms for determining the start-point and end-point of sounds. The proposed multistage delineation algorithm is capable of improving the delineation accuracy under time-varying amplitudes of heart sounds and various types of murmurs. The proposed method has significant potential applications in heart sounds and murmurs classification systems.
References
-
-
1)
-
7. Kwak, C., Kwon, O.-W.: ‘Cardiac disorder classification by heart sound signals using murmur likelihood and hidden Markov model state likelihood’, IET Signal Process., 2012, 6, (4), pp. 326–334 (doi: 10.1049/iet-spr.2011.0170).
-
2)
-
19. Olmez, T., Dokur, Z.: ‘Classification of heart sounds using an artificial neural network’, Pattern Recognit. Lett., 2003, 24, pp. 617–629 (doi: 10.1016/S0167-8655(02)00281-7).
-
3)
-
8. Boutana, D., Benidir, M., Barkat, B.: ‘Segmentation and identification of some pathological phonocardiogram signals using time-frequency analysis’, IET Signal Process., 2011, 5, (6), pp. 527–537 (doi: 10.1049/iet-spr.2010.0013).
-
4)
-
9. Wang, Y., Lia, W., Zhoua, J., et al: ‘Identification of the normal and abnormal heart sounds using wavelet-time entropy features based on OMS-WPD’, Future Gener. Comput. Syst., 2014, 37, pp. 488–495 (doi: 10.1016/j.future.2014.02.009).
-
5)
-
15. Gill, D., Gavrieli, N., Intrator, N.: ‘Detection and identification of heart sounds using homomorphic envelogram and self-organizing probabilistic model’, Comput. Cardiol., 2005, 32, pp. 957–960.
-
6)
-
24. Manikandan, M.S., Soman, K.P., Dandapat, S.: ‘Quality-driven wavelet based PCG signal coding for wireless cardiac patient monitoring’. Proc. First Int. Conf. Wireless Technologies for Humanitarian Relief, 2011, pp. 519–526.
-
7)
-
6. Ahlstrom, C., Huit, P., Ask, P.: ‘Detection of the 3rd heart sound using recurrence time statics’. Proc. ICASSP, 2006, pp. 1040–1043.
-
8)
-
4. Papadaniil, C., Hadjileontiadis, L.: ‘Efficient heart sound segmentation and extraction using ensemble empirical mode decomposition and kurtosis features’, IEEE J. Biomed. Health Inf., 2014, 18, (4), pp. 1138–1152 (doi: 10.1109/JBHI.2013.2294399).
-
9)
-
10)
-
11. Sabarimalai Manikandan, M., Soman, K.P.: ‘Robust heart sound activity detection in noisy environments’, Electron. Lett., 2010, 46, (16), pp. 1100–1102 (doi: 10.1049/el.2010.1201).
-
11)
-
2. Rangayyan, R.M.: ‘Biomedical signal analysis: a case-study approach’ (Wiley-IEEE Press, 2001).
-
12)
-
13)
-
5. Tseng, Y.-L., Ko, P.-Y., Jaw, F.-S.: ‘Detection of the third and fourth heart sounds using Hilbert–Huang transform’, Biomed. Eng. Online, 2012, 11, (8), pp. 1–13.
-
14)
-
3. Lehner, R.J., Rangayyan, R.M.: ‘A three-channel microcomputer system for segmentation and characterization of the phonocardiogram’, IEEE Trans. Biomed. Eng., 1987, BME-34, (6), pp. 485–489 (doi: 10.1109/TBME.1987.326060).
-
15)
-
13. Haghighi-Mood, A., Torry, J.N.: ‘A sub-band energy tracking algorithm for heart sound segmentation’, Comput. Cardiol., 1995, pp. 501–504.
-
16)
-
17. Nivitha Varghees, V., Ramachandran, K.I.: ‘A novel heart sound activity detection framework for automated heart sound analysis’, Biomed. Signal Process. Control, 2014, 13, pp. 174–188 (doi: 10.1016/j.bspc.2014.05.002).
-
17)
-
12. Iwata, A., Ishii, N., Suzumura, N., et al: ‘Algorithm for detecting the first and the second heart sounds by spectral tracking’, Med. Biol. Eng. Comput., 1980, 18, (1), pp. 19–26 (doi: 10.1007/BF02442475).
-
18)
-
19)
-
20. Kathirvel, P., Manikandan, M.S., Prasanna, S.R.M., et al: ‘An efficient R-peak detection based on new nonlinear transformation and first-order Gaussian differentiator’, Cardiovasc. Eng. Technol., 2012, 2, (4), pp. 408–425 (doi: 10.1007/s13239-011-0065-3).
-
20)
-
14. Liang, H., Lukkarinen, S., Hartimo, I.: ‘Heart sound segmentation algorithm based on heart sound envelolgram’, Comput. Cardiol., 1997, 24, pp. 105–108.
-
21)
-
16. Alajarin, J.M., Merino, R.R.: ‘Efficient method for events detection in phonocardiographic signals’, Proc. SPIE Bioengineered Bioinspired Syst. II, 2005, 5839, pp. 398–409 (doi: 10.1117/12.608203).
-
22)
-
10. Safara, F., Doraisamy, S., Azman, A., et al: ‘Multi-level basis selection of wavelet packet decomposition tree for heart sound classification’, Comput. Biol. Med., 2013, 43, (10), pp. 1407–1414 (doi: 10.1016/j.compbiomed.2013.06.016).
-
23)
-
1. Walker, H.K., Hall, W.D., Hurst, J.W.: ‘Clinical methods: the history, physical, and laboratory examinations’ (Butterworths, Boston, 1990, 3rd edn.).
-
24)
-
18. Choi, S., Jiang, Z.: ‘Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique’, Comput. Biol. Med., 2010, 40, (1), pp. 8–20 (doi: 10.1016/j.compbiomed.2009.10.003).
http://iet.metastore.ingenta.com/content/journals/10.1049/htl.2015.0010
Related content
content/journals/10.1049/htl.2015.0010
pub_keyword,iet_inspecKeyword,pub_concept
6
6