Open Access Open Access  Restricted Access Subscription or Fee Access

A New Compression Scheme Based on Adaptive Vector Quantization and Singular Value Decomposition

(*) Corresponding author

Authors' affiliations



This paper presents a new technique for electrocardiogram data compression. The proposed method is based on the vector quantization and the singular value decomposition. First, the singular value decomposition is performed on the two-dimensional representation of the electrocardiogram signal and then a limited number of the weighted right singular vectors is quantized using the vector quantization. In addition, a residual encoding based on the singular value decomposition of the residual error is proposed. Consequently, a low reconstruction error was obtained. A new scheme is also proposed for the generation of the vector quantization codebook, it is based on the Lindth-Buzo-Gray algorithm, but in the proposed technique, at each vector quantization stage, a new codebook is generated. This latter is only produced from the retained singular vectors. Several tests were conducted using arrhythmia database of the Massachusetts Institute of Technology-Beth Israel Hospital. The obtained results were very satisfactory. Unlike most existing methods, in the proposed scheme, the codebook storage was taken into account during the calculation of the compression ratio which was not affected, since high compression ratios were obtained. For instance, a compression ratio of 102.54 was obtained in the case of the percent root mean square difference of 1.02%.
Copyright © 2016 Praise Worthy Prize - All rights reserved.


Codebook; Electrocardiogram Compression; Residual Encoding; Singular Value Decomposition; Vector Quantization

Full Text:



Deiaa Eid, Amr Yousef, Ali Elrashidi, ECG Signal Transmissions Performance over Wearable Wireless Sensor Networks, Procedia Computer Science, Vol.65, pp. 412-421, 2015.

A.Nait-Ali,C.Cavaro-Ménard, Compression des images et des signaux médicaux (Hermes Science, Lavoisier 2007).

M. Sabarimalai Manikandan, S. Dandapat, Wavelet-based electrocardiogram signal compression methods and their performances: A prospective review, Biomedical Signal Processing and Control, vol. 14, pp. 73–107, 2014.

S.M.S. Jalaleddine, C.G. Hutchens, R.D. Strattan, W.A. Coberly, ECG data compression techniques-a unified approach, IEEE Transactions on Biomedical Engineering, Vol. 37, n. 4, pp. 329–343,1990.

Chia-Chun Sun, Shen-Chuan Tai, Beat-Based ECG Compression Using Gain-Shape Vector Quantization, IEEE Transactions on Biomedical Engineering, Vol. 52, n. 11, pp. 1882-1888, 2005.

M.Sabarimalai Manikandan, S.Dandapat, Wavelet-Threshold based ECG Compression with Smooth Retrieved Quality for Telecardiology, Proceedings of the IEEE Fourth International Conference on Intelligent Sensing and Information Processing, ICISIP 2006, (Pages: 138 - 143, Year of Publication: 2006, ISBN: 1-4244-0612-9).

Ban-Hoe Kwan, Raveendran Paramesran , Comparison between Legendre moments and DCT in ECG compression, IEEE Region 10 Conference (Volume A), TENCON 2004, (Pages: 167 – 170, Year of Publication: 2004, ISBN: 0-7803-8560-8).

Shen-Chuan Tai, Chia-Chun Sun, Wen-Chien Yan, A 2-D ECG Compression Method Based on Wavelet Transform and Modified SPIHT, IEEE Transactions on Biomedical Engineering, Vol. 52, n. 6, pp. 999-1008, 2005.

Hanwoo Lee, Kevin M. Buckley, ECG Data Compression Using Cut and Align Beats Approach and 2-D Transforms, IEEE Transactions on Biomedical Engineering, Vol. 46, n. 5, pp. 556-564, May 1999.

Jyh-Jong Wei, Chuang-Jan Chang, Nai-Kuan Chou, Gwo-Jen Jan, ECG Data Compression Using Truncated Singular Value Decomposition, IEEE Transactions on Information Technology In Biomedicine, Vol. 5, n. 4, pp. 290-299, 2001.

Ranjeet Kumar, A. Kumar, G.K. Singh, Electrocardiogram signal compression based on singular value decomposition (SVD) and adaptive scanning wavelet difference reduction (ASWDR) technique, International Journal of Electronics and Communications (AEÜ), Vol. 69, 1810-1822, 2015.

P.P.Kanjilal, S.Palit, G.Saha, Fetal ECG Extraction from Single Channel Maternal ECG Using Singular Value Decomposition, IEEE Transactions on Biomedical Engineering, Vol. 44, n. 1, pp. 51-59, 1997.

Xingyuan Wang, Juan Meng, A 2-D ECG compression algorithm based on wavelet transform and vector quantization, Digital Signal Processing, Vol. 18, n. 2, pp. 179–188, 2008.

Makhoul J., Roucos S., Gish Herbert , Vector Quantization in Speech Coding, Proceedings of the IEEE, Vol. 73, n. 11, (Pages: 1551 - 1588, Year of Publication: 1985, ISSN: 0018-9219).

R.M. Gray, Vector quantization, IEEE ASSP Magazine, Vol. 1, n. 2, pp. 4 – 29, 1984.

Yoseph Linde, Andres Buzo, Robert M.Gray, An Algorithm for Vector Quantizer Design, IEEE Transactions on Communications, Vol. 28, n. 1, pp. 84-95, 1980.

F.Gritzali, Towards a Generalized Scheme for QRS Detection In ECG Waveforms, Signal Processing, Vol.15, n. 2, pp. 183-192, 1988.

B.A. Rajoub, An efficient coding algorithm for the compression of ECG signals using the wavelet transform , IEEE Transactions on Biomedical Engineering, Vol. 49, n. 4, pp. 355 - 362, 2002.

Alam M.S., Rahim N.M.S., Compression of ECG Signal Based on its Deviation from a Reference Signal using Discrete Cosine Transform, 5th International Conference on Electrical and Computer Engineering, ICECE 2008, (Pages: 53 – 58, Year of Publication: 2008, ISBN: 978-1-4244-2014-8).

Zhitao Lu, Dong Youn Kim, William A. Pearlman, Wavelet Compression of ECG Signals by the Set Partitioning in Hierarchical Trees Algorithm, IEEE Transactions on Biomedical Engineering, Vol. 47, n. 7, pp. 849 - 856, 2000.

Alessandro Adamo, Giuliano Grossi, Raffaella Lanzarotti, Jianyi Lin, ECG compression retaining the best natural basis k-coefficients via sparse decomposition, Biomedical Signal Processing and Control, Vol. 15, pp. 11–17, 2015.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2022 Praise Worthy Prize