Abstract
This paper proposes a cost effective solution for improving the effectiveness of e-auscultation. Auscultation is the most difficult skill for a doctor, since it can be acquired only through experience. The heart sound mixtures are captured by placing the four numbers of sensors at appropriate auscultation area in the body. These sound mixtures are separated to its relevant components by a statistical method independent component analysis. The separated heartbeat sounds can be further processed or can be stored for future reference. This idea can be used for making a low cost, easy to use portable instrument which will be beneficial to people living in remote areas and are unable to take the advantage of advanced diagnosis methods.
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References
L.S. Bickley, B. Bates, P.G. Szilagyi, Bates' Guide to Physical Examination and History Taking, 9th Revised edn. (Lippincott Williams and Wilkins, 2005)
J.B. Kostis, Mechanisms of heart sounds. Am. Heart J. 89, 546 (1975)
R.S. Geethu, S.N. George, M.K. Krishna, A proposal for source separation of heartbeat sounds and its FPGA implementation. In Proceedings of IEEE International Conference on Communication Systems and Network Technologies (CSNT), pp. 755–758 (2012)
M.K. Krishna, K.V. Pramod, R.S. Geethu, Source separation of heartbeat sounds. MES JTM, 1(2), 65–71
S. Makino, S. Araki, R. Mukai, H. Sawada, Audio Source Separation Based on Independent Component Analysis. ISCAS 668–671 (2004)
T.W. Lee, A.J. Bell, R. Orglmeister, Blind source separation of real world signals. Neural Netw. 4, 2129–2134 (1997)
X. Hongyan, H. Jinyong, Blind Separation of Weak Aignals Under Chaotic Background, 978-1-4244-4134-1/09©2009 IEEE
J.-F. Cardoso, Blind signal separation: statistical principles. In Proceedings of the IEEE, Special Issue on Blind Identification and Estimation, vol. 9, pp. 2009–2025 (1998)
A. Hyvarinen, Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10, 626–634 (1999)
Hyvarinen, J. Karhunen, E. Oja, Independent component analysis: algorithms and applications. Neural Netw. 13, 411–430 (2000)
A. Hyvarinen, E. Oja, Independent Component Analysis: A Tutorial. http://www.cis.hut.fi/projects/ica
Hyvarinen et.al. Independent Component Analysis (Wiley, New York, 2001)
J.V. Stone, Independent Component Analysis: A Tutorial Introduction (The MIT Press, Cambridge)
J.-C. Chien, M.-C. Huang, Y.-D. Lin, F.-C. Chong, A study of heart and lung sound separation by independent component analysis technique. In Proceedings of the 28th IEEE EMBS Annual International Conference, 2006, pp. 5708–5711
K. Usman, M.A. Sadiq, H. Juzoji, I. Nakajima, A Study of Heartbeat Sound Separation Using Independent Component Analysis Technique. Enterprise Networking and Comp. in Healthcare Industry. Proceedings Volume, Issue, 28–29 June 2004, pp. 92–95
X. Giannakopoulos, J. Karhunen, E. Oja, Experimental comparison of neural ICA algorithms. In: Proceedings on International Conference on Artificial Neural Networks (ICANN’98), Skövde, Sweden, pp. 651–656 (1998)
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Geethu, R.S., Krishnakumar, M., Pramod, K.V. et al. Source Separation of Heartbeat Sounds for Effective E-Auscultation. J. Inst. Eng. India Ser. B 97, 69–75 (2016). https://doi.org/10.1007/s40031-015-0186-4
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DOI: https://doi.org/10.1007/s40031-015-0186-4