Interpretation of Heart Sound Signal through Automated Artifact-Free Segmentation
DOI:
https://doi.org/10.17140/HROJ-2-105Keywords:
Heart Sounds; PCG Signal, Discrete Wavelet Packet Transform, Segmentation, Artifact removal, Artifact-free subsequenceAbstract
Purpose: Digital recording of heart sounds commonly known as Phonocardiogram (PCG)
signal, is a convenient primary diagnostic tool for analyzing condition of heart. Phonocardiogram
aids physicians to visualize the acoustic energies that results from mechanical
aspect of cardiac activity. PCG signal cycle segmentation is an essential processing step towards
heart sound signal analysis. Sound artifacts due to inappropriate placement of stethoscope,
body movement, cough etc. makes segmentation difficult. Artifact-free segmented heart
sound cycles are convenient for physicians to interpret and it is also useful for computerized
automated classification of abnormality.
Methods: We have developed a framework which selects good quality heart sound subsequences
which are artifact-free and reused the features involved in this processing in segmentation.
In this work, we have used information contained in frequency subbands by decomposing
the signal using Discrete Wavelet Packet Transform (DWPT). The algorithm identifies the
parts of the signal where artifacts are prominent and it also detects major events in heart sound
cycles.
Results: The algorithm shows good results when tested on normal and five commonly occurring
pathological heart sound signals. An average accuracy of 93.71% is registered for artifactfree
subsequence selection process. The cycle segmentation algorithm gives an accuracy of
98.36%, 98.18% and 93.97% respectively for three databases used in the experiment.
Conclusions: The work provides a solution for artifact-free segmentation of heart sound cycles
to assist interpretation of heart sound by physicians in objective analysis through recording
in a computer. It is also useful for development of an automated decision support system on
heart sound abnormality.
MAIN KEY FINDINGS
• Artifact free subsequence detection is preferable over attempt to reduce the effect of artifacts
due to overlap of information content.
• DWPT is useful for detection of artifact contaminated subsequences due to its ability to provide
for more detailed information of higher frequency components.
• DWPT features of subsequence detection can be reused for automatic segmentation of heart
sound.
• The artifact-free segmented heart sound cycle detection system can work in real-time.
References
Alam U, Asghar O, Khan SQ, Hayat S, Malik RA. Cardiac auscultation: an essential clinical skill in decline. Br J Cardiol. 2010; 17(1): 8-10.
Mangione S, Nieman LZ. Cardiac auscultatory skills of inter-nal medicine and family practice trainees: a comparison of diag-nostic proficiency. JAMA. 1997; 278(9): 717-722. doi: 10.1001/ jama.1997.03550090041030
Tang H, Li T, Park Y, Qiu T. Separation of heart sound sig-nal from noise in joint cycle frequency-time-frequency do-mains based on fuzzy detection. Biomedical Engineering, IEEE Transactions on. 2010; 57(10): 2438-2447. doi: 10.1109/ TBME.2010.2051225
Sweeney KT, Leamy DJ, Ward TE, McLoone S. Intelligent artifact classification for ambulatory physiological signals. Engi-neering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. 2010; 6349-6352.
Beritelli F, Spadaccini A. Heart sounds quality analysis for au-tomatic cardiac biometry applications, in Information Forensics and Security, 2009. WIFS 2009. First IEEE International Work-shop on IEEE. 2009; 61-65.
Sweeney KT, Ward TE, McLoone SF. Artifact removal in physiological signals-practices and possibilities. Information Technology in Biomedicine, IEEE Transactions on. 2012; 16(3): 488-500. doi: 10.1109/TITB.2012.2188536
Ari S. Analysis and characterization of phonocardiogram sig-nal for diagnosing valvular heart disease, Ph.D. dissertation, IIT Kharagpur, India, August 2009.
Li T, Qiu T, Tang H. Optimum heart sound signal selection based on the cyclostationary property. Computers in biology and medicine. 2013; 43(6): 607-612.
Choi S, Shin Y, Park H-K. Selection of wavelet packet mea-sures for insufficiency murmur identification, Expert Systems with Applications. 2011; 38(4): 4264-4271.
Kumar D, Carvalho P, Antunes M, Paiva R, Henriques J. Noise detection during heart sound recording using periodicity signatures, Physiological measurement. 2011; 32(5): 599.
Friedman HH. Diagnostic electrocardiography and vectorcar-diography. McGraw-Hill Inc., New York, 1977.
Kumar D, Carvalho Pd, Antunes M, et al. Wavelet transform and simplicity based heart murmur segmentation, in Comput-ers in Cardiology, 2006. IEEE. 2006: 173-176. doi: 10.1016/j. compbiomed.2013.03.002
Hedayioglu F, Jafari M, Mattos S, Plumbley M, Coimbra M. Denoising and segmentation of the second heart sound us-ing matching pursuit, in Engineering in International Conference of Medicine and Biology Society, San Diego, CA, USA. 2012: 3440-3443.
Ari S, Kumar P, Saha G. A robust heart sound segmentation algorithm for commonly occurring heart valve diseases. Journal of Medical Engineering & Technology. 2008; 32(6): 456-465. doi: 10.1080/03091900601015162
Safara F, Doraisamy S, Azman A, Jantan A, Ranga S. Wavelet packet entropy for heart murmurs classification. Advances in bio-informatics. 2012; 2012. doi: 10.1155/2012/327269
Choi S. Detection of valvular heart disorders using wavelet packet decomposition and support vector machine. Expert Sys-tems with Applications. 2008; 35(4): 1679-1687. doi: 10.1016/j. eswa.2007.08.078
Choi S, Shin Y, Park H-K. Selection of wavelet packet mea-sures for insufficiency murmur identification. Expert Systems with Applications. 2011; 38(4): 4264-4271. doi: 10.1016/j. eswa.2010.09.094
Feldman M. Nonparametric identification of asymmetric non-linear vibration systems with the hilbert transform. Journal of Sound and Vibration. 2012; 331(14): 3386-3396. doi: 10.1016/j. jsv.2012.02.025
Mondal A, Kumar AK, Bhattacharya P, Saha G. Boundary estimation of cardiac events s1 and s2 based on hilbert transform and adaptive thresholding approach. Medical Informatics and Telemedicine (ICMIT), 2013 Indian Conference on. IEEE. 2013: 43-47. doi: 10.1109/IndianCMIT.2013.6529406
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All content published in Heart Research (HROJ) is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows anyone to copy, distribute, transmit, and adapt the work — including for commercial purposes — provided appropriate credit is given to the original author(s) and source.