Research Article | Open Access
Intelligent Identification of Childhood Musical Murmurs
Heart murmurs are often the first signs of heart valvular disorders. However, most heart murmurs detected in children are innocent musical murmurs (also called Still's murmurs), which should be distinguished from other murmur types that are mostly pathological, such as regurgitant, obstructive, and flow murmurs. In order to reduce both unnecessary healthcare expenditures and parental anxiety, this study aims to develop algorithms for intelligently identifying musical murmurs in children. Discrete wavelet transform was applied to phonocardiographic signals to extract features. Singular value decomposition was applied on the matrix derived from continuous wavelet transform to extract extra features. The sequential forward feature selection algorithm was then utilized to select significant features. Musical murmurs were subsequently differentiated via a classification procedure consisting of three classification techniques: discriminant analysis, support vector machine, and artificial neural network. The results of 89.02% sensitivity, 84.76% specificity and 87.36% classification accuracy were achieved.
- M. Heron, D. Hoyert, S. Murphy, J. Xu, K. Kochanek, and B. Tejada-Vera, “Deaths: Final Data for 2006,” National Vital Statistics Reports, vol. 57, no. 14, 2009.
- D. Lloyd-Jones, R. Adams, T. Brown et al., “Heart Disease and Stroke Statistics—2010 Update: a Report from the American Heart Association,” Circulation, vol. 121, no. 7, pp. e46–e215, 2010.
- W. Myint and B. Dillard, “An electronic stethoscope with diagnosis capability,” in Conf Proc the 33rd Southeastern Symposium on System Theory, pp. 133–137, 2001.
- A. Tilkian and M. Conover, Understanding Heart Sounds and Murmurs: with an Introduction to Lung Sounds, Saunders, Philadelphia, 4th edition, 2001.
- A. Pelech, “The Physiology of Cardiac Auscultation,” Pediatric Clinics of North Amrica, vol. 51, pp. 1515–1535, 2004.
- A. Noponen, S. Lukkarinen, A. Angerla, K. Sikio, and R. Sepponen, “How to Recognize the Innocent Vibratory Murmur?” Computers in Cardiology, vol. 27, pp. 561–564, 2000.
- B. McCrindle, K. Shaffer, J. Kan, K. Zahka, S. Rowe, and I. Kidd, “An Evaluation of Parental Concerns and Misperceptions about Heart Murmurs,” Clin Pediatr, vol. 34, pp. 25–31, 1995.
- R. Geggel, L. Horowitz, E. Brown, M. Parsons, P. Wang, and D. Fulton, “Parental Anxiety Associated with Referral of a Child to a Pediatric Cardiologist for Evaluation of a Still's Murmur,” J Pediatr, vol. 140, pp. 747–752, 2002.
- I. Haney, M. Ipp, W. Feldmen, and B. McCrindle, “Accuracy of Clinical Assessment of Heart Murmurs by Office Based (General Practice) Paediatricians,” Arch Dis Child, vol. 81, no. 5, pp. 409–412, 1999.
- P. Gaskin, S. Owens, N. Talner, S. Sanders, and J. Li, “Clinical Auscultation Skills in Pediatric Residents,” Pediatrics, vol. 105, pp. 1184–1187, 2000.
- J. Vukanovic-Criley, S. Criley, C. Warde et al., “Competency in Cardiac Examination Skills in Medical Students, Trainees, Physicians, and Faculty: a Multicenter Study,” Arch Intern Med, vol. 166, pp. 610–616, 2006.
- A. Pease, “If the Heart could Speak. Pictures of the Future,” 2001, 60-61.
- E. Etchells, C. Bell, and K. Robb, “Does this Patient have an Abnormal Systolic Murmur?” JAMA, vol. 277, pp. 564–571, 1997.
- A. Noponen, S. Lukkarinen, A. Angerla, and R. Sepponen, “Phono-spectrographic Analysis of Heart Murmur in Children,” BMC Pediatrics, vol. 7:23, 2007.
- W. Thompson, C. Hayek, C. Tuchinda, J. Telford, and J. Lombardo, “Automated Cardiac Auscultation for Detection of Pathologic Heart Murmurs,” Pediatr Cardiol, vol. 22, pp. 373–379, 2001.
- N. Andrisevic, K. Ejaz, F. Rios-Gutierrez, R. Alba-Flores, G. Nordehn, and S. Burns, “Detection of Heart Murmurs Using Wavelet Analysis and Artificial Neural Networks,” Journal of Biomedical Engineering, vol. 127, no. 6, pp. 899–904, 2005.
- S. Strunic, F. Rios-Gutierrez, R. Alba-Flores, G. Nordehn, and S. Burns, “Detection and classification of cardiac murmurs using segmentation techniques and artificial neural networks,” in Conf Proc IEEE Symposium on Computational Intelligence and Data Mining, pp. 397–404, 2007.
- C. Ahlstrom, P. Hult, P. Rask et al., “Feature Extraction for Systolic Heart Murmur Classification,” Annals of Biomedical Engineering, vol. 34, no. 11, pp. 1666–1677, 2006.
- J. de Vos and M. Blanckenberg, “Automated Pediatric Cardiac Auscultation,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 2, pp. 244–252, 2007.
- P. White, W. Collis, and A. Salmon, “Analysing heart murmurs using time-frequency methods,” in Conf Proc IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, pp. 385–388, Paris, France, 1996.
- Z. Sharif, M. Zainal, A. Sha'ameri, and S. Salleh, “Analysis and classification of heart sounds and murmurs based on the instantaneous energy and frequency estimations,” in Conf Proc Tencon, vol. 2, pp. 130–134, Kuala Lumpur, Malaysia, 2000.
- S. Omran and M. Tayel, “A heart sound segmentation and feature extraction algorithm using wavelets,” in Conf Proc the First International Symposium on Control, Communications and Signal Processing, pp. 235–238, 2004.
- C. Ahlstrom, K. Höglund, P. Hult, J. Häggström, C. Kvart, and P. Ask, “Distinguishing Innocent Murmurs from Murmurs Caused by Aortic Stenosis by Recurrence Quantification Analysis,” International Journal of Biomedical Sciences, vol. 1, no. 1, pp. 213–218, 2006.
- T. Reed, N. Reed, and P. Fritzson, “Heart Sound Analysis for Symptom Detection and Computer-Aided Diagnosis,” Simulation Modelling Practice and Theory, vol. 12, pp. 129–146, 2004.
- T. Ölmez and Z. Dokur, “Classification of Heart Sounds Using an Artificial Neural Network,” Pattern Recognition Letters, vol. 24, pp. 617–629, 2003.
- C. Gupta, R. Palaniappan, S. Swaninathan, and S. Krishnan, “Neural Network Classification of Homomorphic Segmented Heart Sounds,” Applied Soft Computing, vol. 7, pp. 286–297, 2007.
- Ears On!, http://earson.ca/what.html, Accessed Dec 5, 2011.
- S. Ari and G. Saha, “In Search of an Optimization Technique for Artificial Neural Network to Classify Abnormal Heart Sounds,” Applied Soft Computing, vol. 9, pp. 330–340, 2009.
- M. Vetterli and J. Kovačević, Wavelets and Subband Coding, Prentice Hall, New Jersey, 1995.
- H. Hassanpour, M. Mesbah, and B. Boashash, “Time-Frequency Feature Extraction of Newborn EEG Seizure Using SVD-Based Techniques,” EURASIP Journal on Applied Signal Processing, vol. 16, pp. 2544–2554, 2004.
- G. Nakos and D. Joyner, Linear Algebra with Applications, Brooks/Cole Publishing Company, Pacific Grove, California, 1998.
- S. Theodoridis and K. Koutroumbas, Pattern Recognition, Academic Press, 4th edition, 2009.
- T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2001.
- O. Doyle, A. Temko, W. Marnane, G. Lightbody, and G. Boylan, “Heart Rate Based Automatic Seizure Detection in the Newborn,” Medical Engineering & Physics, vol. 32, pp. 829–839, 2010.
- H. Cao, L. J. Eshelman, L. Nielsen, B. D. Gross, M. Saeed, and J. J. Frassica, “Hemodynamic Instability Prediction through Continuous Multiparameter Monitoring in ICU,” Journal of Healthcare Engineering, vol. 1, no. 4, pp. 509–534, 2010.
Copyright © 2012 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.