Regarding Chapter 6, we improved the algorithms proposed by Liang et al.[39, 38]. Their research had some good ideas such as the envelogram for detecting the peaks but also had some room for improvement as the filtering performed by the algorithms is not suitable for real clinical noisy environments (only 34% of the collected auscultations were useful for the algorithm). We also realized that the pick-peaking process could be improved, since it does not analyze the frequency features of the peaks, and puts two statistically different components (S1 and S2) into the same statistical measure to calculate the low and high-time limit. Also, it has quite a large number of static parameters that need to be tuned. The difference between our results and the results obtained by Liang et al. can be due to the fact that their experiments seem to have been performed on a very controlled environment (the situation the authors report as reasons for incorrect classification, such as speech, crying, etc. are common on the uncontrolled environment of clinical scenarios).
CHAPTER 7. CONCLUSIONS AND FUTURE WORK 59 4. Heart cycle classification: Literature shows a variety of techniques that could improve the identification of S1 and S2, such as Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs)[61].
Also, as the sharper reader has noticed, from the medical description of heart sounds and murmurs, it is important to segment not only S1 and S2, but also A2 and P2. Our survey, on the other hand could not find any record of an automated method for this kind of segmentation. This would be a natural next step of the work here presented.
Appendix A
This section describes the data annotated by the clinicians regarding the patients whose auscultations where segmented by the algorithms presented in this thesis.
Age (months)
Weight (Kg)
Height (m)
1st and 2nd Heart Sound Remarks
Number of Auscultations
12 1 0.2 Normal, fixed
splitting
1
108 10 0.9 Normal 2
- - - Normal 1
- - - Normal 1
- 14.8 0.87 Normal 1
60 19.8 1.11 Normal 2
60 21 1.15 Normal 2
96 30 1.26 Normal 1
132 43 1.49 Normal 2
8 3.6 0.485 Normal 1
288 48 1.63 Normal 1
84 - - Normal 1
- 62.5 1.86 Normal 1
- 23 1.19 Normal 2
48 - - Normal 2
264 30 1.38 Normal 1
156 52 1.63 Normal 1
1 3.7 0.52 Normal 1
168 54 1.69 Normal 1
Table 1: Description of the patients whose auscultations where segmented by the algorithm in chapter 6.
60
Bibliography
[1] V. A. McKusick,Cardiovascular sound in Heart and Disease. Baltimore:
Wiliams & Wilkins, Jan. 1958, the History of Cardiovascular Sound.
[2] J. Forbes,A Treatise on the Disease of the Chest. Underwood, 1821.
[3] B. G. E. and D. Pasquale,A history of electrocardiography. Norman Pub-lishing, 1990.
[4] M. E. Tavel, Clinical Phonocardiography and External Pulse Recording.
Year Book Medical Publishers, 1972.
[5] O. Weiss and G. Joachin, “Registrierung von hertzönen und herzgeräuschen mittels des phonoskops und ihre beziehungen zum elektrokardiogramm,”
Ztschr. klin. Med, vol. 73, p. 240, 1911.
[6] B. Randell,The COLOSSUS. University of Newcastle upon Tyne, Com-puting Laboratory, 1976.
[7] L. Floridi,Philosophy and computing: An introduction. Routledge, 1999.
[8] E. H. Shortliffe, Ed.,Biomedical Informatics - Computer Applications in Health Care and Biomedicine. Springer, 2006.
[9] D. W. Simborg and M. Chadwick, “Local area networks and the hospital.”
Computers and Biomedical Research, vol. 16(3), pp. 247–259, 1983.
[10] Hl7 home. [Online]. Available: http://www.hl7.org/about/index.cfm [11] E. Berner, D. Detmer, and D. Simborg, “Will the wave finally break? a brief
view of the adoption of electronic medical records in the united states:,”
Journal of American Medical Informatics Association, vol. 12, pp. 3–7, Feb 2005.
[12] R. Miller, M. McNeil, S. Challinor, F. Masarie, and J. Myers, “The internist-1/quick medical reference project-status report,” Western Journal Med., vol. 145, pp. 816–22, 1986.
61
[13] G. Barnett, J. Cimino, J. Hupp, and E. Hoffer, “Dxplain - an evolving diagnostic decision-support system,” JAMA, vol. 258, pp. 67–74, 1987.
[14] W. H. Jr, “Iliad: moving medical decision-making into new frontiers.” Meth-ods Inf Med, vol. 28, pp. 370–2, 1989.
[15] C. McDonald, S. Hui, D. Smith, W. Tierney, S. Cohen, M. Weinberger, and G. McCabe, “Reminders to physicians from an introspective computer medical record. a two-year randomized trial.” Ann. Intern. Med., vol. 100, pp. 130–8, 1984.
[16] M. Korcok, “Computer diagnostics: Technology of the future,” Can. Med.
Assoc. J., vol. 133, pp. 231–235, 1985.
[17] G. Lodwick, C. Haun, W. Smith, R. Keller, and E. Robertson, “Computer diagnosis of primary bone tumors: a preliminary report,”Radiology, vol. 80, no. 2, pp. 273–275, 1963.
[18] P. Meyers, C. Nice Jr, H. Becker, W. Nettleton Jr, J. Sweeney, and G. Meck-stroth, “Automated Computer Analysis of Radiographic Images.” Radiol-ogy, vol. 83, p. 1029, 1964.
[19] F. Winsberg, M. Elkin, J. Macy, V. Bordaz, and W. Weymouth, “Detection of radiographic abnormalities in mammograms by means of optical scanning and computer analysis,” Radiology, vol. 89, no. 2, pp. 211–215, 1967.
[20] R. Kruger, J. Townes, D. Hall, S. Dwyer, and G. Lodwick, “Automated radiographic diagnosis via feature extraction and classification of cardiac size and shape descriptors,”IEEE Transactions on Biomedical Engineering, pp. 174–186, 1972.
[21] R. Kruger, W. Thompson, and A. Turner, “Computer diagnosis of pneu-moconiosis,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 4, no. 1, pp. 40–49, 1974.
[22] J. Toriwaki, Y. Suenaga, T. Negoro, and T. Fukumura, “Pattern recognition of chest X-ray images,” Comput. Graph. Image Processing, vol. 2, pp. 252–
271, 1973.
[23] K. Doi, “Computer-aided diagnosis in medical imaging: historical review, current status and future potential,” Computerized Medical Imaging and Graphics, vol. 31, no. 4-5, pp. 198–211, 2007.
[24] E. RL., “Attempt to use computers as diagnostic aids in medical deci-sion making: a 30-year experience.” Perspectives in Biology and Medicine, vol. 35, pp. 207–218, 1992.
BIBLIOGRAPHY 63 [25] R. Miller, “Medical diagnostic decision support systems–past, present, and future: a threaded bibliography and brief commentary.” Journal of the American Medical Informatics Association, vol. 1, no. 1, p. 8, 1994.
[26] G. Borden and K. Harris,Speech science primer: physiology, acoustics, and perception of speech, Baltimore, Ed. Williams & Wilkins, 1994.
[27] e. a. Paul A. Iazzo,Handbook of Cardiac Anatomy, Physiology, and Devices, P. A. Iazzo, Ed. Humana Press Totowa, New Jersey, 2005.
[28] W. G. C. S. Germann,Principles of Human Physiology. Pearson Educa-tion/Benjamin Cummings, 2002.
[29] A. A. Luisada,From Auscultation to phonocardiograpy. The C. V. Mosby Company, 1965.
[30] M. E. Tavel,Clinical phonocardiography and external pulse recording. Year Book Medical Publishers, 1972.
[31] P. S. Addison,The Illustrated Wavelet Transform Handbook. Institute of Physics Publishing Bristol and Philadelphia, 2002.
[32] M. W. Frazier, An Introduction to Wavelets Through Linear Algebra.
Springer, 1999.
[33] L.-G. Durand and P. Pibarot, “Digital signal processing of the phonocar-diogram: Review of the most recent advancements,” Critical Reviews in Biomedical Engineering, 1995.
[34] M. Bredesen and E. Schmerler, “Intelligent stethoscope,” US Patent 5,010,889, 1991.
[35] F. Hedayioglu, S. Mattos, L. Moser, and M. de Lima, “Development of a tele-stethoscope and it’s application in pediatric cardiology,” Indian Jour-nal of Experimental Biology, vol. 45, 2007.
[36] D. B. M.E. Tavel and D. Shander, “Enhanced auscultation with a new graphic display system,” vol. 154, p. 893, 1994.
[37] M. Brusco and H. Nazeran, “Development of an intelligent pda-based wear-able digital phonocardiograph,” inProceedings of the 2005 IEEE Engineer-ing in Medicine and Biology 27th Annual Conference, Sepember 2005.
[38] S. L. H. Liang and I. Hartimo, “A heart sound segmentation algorithm using wavelet decomposition and reconstruction,” in 19th International Confer-ence - IEEE/EMBS, Chicago, IL, USA, Oct., Nov. 1997.
[39] H. Liang, S. Lukkarinen, and I. Hartimo, “Heart sound segmentation algo-rithm based on heart sound envelogram,”Computers in Cardiology, vol. 24, 1997.
[40] H. Liang and I. Hartimo, “A heart sound feature extraction algorithm based on wavelet decomposition and reconstruction,” inProc. IEEE EMBS, vol. 20, no. 3, 1998.
[41] M. T. Sherif Omran, “A heart sound segmentation and feature extraction algorithm using wavelet,” inProc. of IEEE MWSCAS ’03, vol. 1, 2003, pp.
27–30.
[42] P. P. JingPing Xu, L.G. Durand, “Nonlinear transient chirp signal modeling of the aortic and pulmonary components of the secound heart sound,”IEEE Transactions on Biomedical Engineering, vol. 47, no. 7, March 2000.
[43] J. Xu, L. Durand, and P. Pibarot, “Extraction of the aortic and pulmonary components of the secound heart sound using nonlinear transient chirp sig-nal model,” IEEE Transactions on Biomedical Engineering, vol. 48, no. 3, March 2001.
[44] V. Nigam and R. Priemer, “A procedure to extract the aortic and the pulmonary sounds from the phonocardiogram,” in Proceedings of the 28th IEEE EMBS Annual International Conference, New York City, USA, Aug 30-Sep 3 2006.
[45] P. M. Bentley, J. T. E. McDonnell, and P. M. Grant, “Classification of native heart valve sounds using the choi-williams time-frequency distribution,”
Biomedical Engineering, IEEE Transactions on, 1995.
[46] P. M. Bentley, P. M. Grant, and J. T. E. McDonnell, “Time-frequency and time-scale techniques for the classification of native and bioprosthetic heart valve sounds,”IEEE Transactions on Biomedical Engineering, vol. 45, no. 1, January 1998.
[47] P. Wang, Y. Kim, and C. B. Soh, “Feature extraction based on mel-scaled wavelet transform for heart sound analysis,” in Engineering in Medicine and Biology Society, 2005.
[48] H. Liang and I. Hartimo, “A feature extraction algorithm based on wavelet packet decomposition for heart sound signals,” inProceedings of the IEEE-SP International Symposium, October 1998.
[49] N. H. Onsy Abdel-Alim and M. A. El-Hanjouri, “Heart diseases diagnosis using heart sounds,” inRadio Science Conference, March 2002.