Summary
The real-time transmission of the electrocardiogram (ECG) in urgent situations can improve the chances of the patient. However, one of the greatest problems involving this kind of telemedicine application is the leakage of network bandwidth. ECG exams may generate too much data, which makes difficult to apply telecardiology systems in real life. This problem motivated many authors to look for efficient techniques of ECG compression, such as: transform approaches, 2-D approaches, similarity approaches and generic approaches. The present work proposes a new hypothesis: neural networks may be applied together with Wavelet transforms to compress the ECG. In this approach, the Wavelet transform acts as a pre-processor element for a multilayer perceptron neural network, trained with the backpropagation algorithm. The original signal was divided in two parts: the “plain” blocks and the “complex” ones. The “plain” blocks were compressed with a 40:1 ratio while the “complex” blocks were compressed with a 5:1 ratio. The use of both compressors guaranteed a compression rate of 28:1, approximately. The process obtained good grades in the quality aspect: percent root mean squared difference (1.846%), maximum error (0.1789) and standard derivation of errors (0.1044).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sedgewick, M.L., Dalziel, K., Watson, J., Carrington, D.J., Cobbe, S.M.: Perfomance of an established system of first responder out-of-hospital defibrillation: the results of the second year of the heartstar Scotland project in the Ulstein style, vol. 6, pp. 75–78 (1993)
Alesanco, A., Olmos, S., Istepanian, R.S.H., García, J.: Enhanced Real-Time ECG Coder for Packetized Telecardiology Applications. IEEE Transactions on Information Technology in Biomedicine 10(2), 229–236 (2006)
Chen, J., Zhang, Y., Shi, X.: ECG Compression Based on Wavelet Transform and Golomb Coding. Electronic Letters 42(2), 322–324 (2006)
Ku, C.T., Wang, H.S., Hung, K.C., Hung, Y.S.: A Novel ECG Data Compression Method Based on Nonrecursive Discrete Periodized Wavelet Transform. IEEE Transactions on Biomedical Engineering 53(12), 2577–2583 (2006)
Kim, B., Yoo, S.K., Lee, M.H.: Wavelet-Based low-Delay ECG Compression Algorithm for Continuous ECG Transmission. IEEE Transactions on Information Technology in Biomedicine 10(1), 77–83 (2006)
Alexandre, E., Pena, A., Sobreira, M.: On the Use of 2-D Coding Techniques for ECG Signals. IEEE Transactions on Information Technology in Biomedicine 10(4), 809–811 (2006)
Chou, H.H., Chen, Y.J., Shiau, Y.C., Kuo, T.S.: An Effective and Efficient Compression Algorithm for ECG Signals with Irregular Periods. IEEE Transactions on Biomedical Engineering 53(6), 1198–1205 (2006)
Henriques, J., Brito, M., Gil, P., Carvalho: Searching for Similarities in Nearly Periodic Signals with Applications to ECG Data Compression. In: 18th International Conference on Pattern Recognition, Hong Kong, pp. 942–945 (2006)
Carvalho, M.B., Silva, A.B., Finamore, W.: Multidimensional Signal Compression using Multiscale Recurrent Patterns. Signal Processing Image and Video Coding beyond Standards 82, 3201–3204 (2002)
Filho, E.B.L., Eduardo, A.B., Junior, W.S.S., Carvalho, M.B.: ECG Compression using Multiscale Recurrent Patterns with Period Normalization. In: Proceedings of the IEEE International Symposium, Greece, p. 4 (2006)
Lu, Z., Kim, D.Y., Pearlman, W.A.: Wavelet Compression of ECG Signals by the Set Partioning in Hierarchial Trees (SPIHIT) Algorithm. IEEE Transactions on Biomedical Engineering 47(7) (2000)
Bilgin, A., Marcellin, M.W., Altbach, M.I.: Compression of Electrocardiogram Signals using JPEG2000 49(4) (November 2003)
Brito, M., Henriques, J., Antunes, M.: A Predictive Adaptive Approach to Generic ECG Data Compression. In: IEEE International Symposium on Intelligent Signal Processing, Portugal, pp. 32–37 (2005)
Kannan, R., Eswaran, C., Sriraam, N.: Neural Networks Methods for ECG Data Compression. In: Proceedings of the 9th International Conference on Neural Information Processing ICONIP 2002, vol. 5 (November 2002)
Zhao, Y., Wang, B., Zhao, W., Dong, L.: Applying incompletely connected feedforward neural network to ambulatory ECG data compression. Electronic Letters 33, 220–221 (1997)
Al-Hukazi, E., Al-Nashash, H.: ECG data compression using Hebbian neural networks. Journal of Medical Engineering & Technology (November 1996)
Physyonet. MIT-BIH ECG Arrythmia Database (2007) (Accessed 20 April 2007), http://www.physionet.org/physiobank/database/mitdb
Beth Israel Hospital Inc (2007) (Accessed 21 April 2007), http://www.bih.harvard.edu
Massachussets Institute of Technology (2007) (Accessed 21 April 2007), http://www.mit.edu
Lee, H., Buckley, K.: ECG Data Compression using Cut and Align Beats Approach and 2-D Transforms. IEEE Transactions on Biomedical Engineering 46(5), 556–564 (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Figueredo, M.V.M., Nievola, J.C., Rogal, S.R., Neto, A.B. (2008). Compression of Electrocardiogram Using Neural Networks and Wavelets. In: Lee, R., Kim, HK. (eds) Computer and Information Science. Studies in Computational Intelligence, vol 131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79187-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-79187-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79186-7
Online ISBN: 978-3-540-79187-4
eBook Packages: EngineeringEngineering (R0)