A Survey on different Compression Techniques for ECG Data Reduction

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Sakshi, Nirvair Neeru

Sakshi and Nirvair Neeru. A Survey on different Compression Techniques for ECG Data Reduction. International Journal of Computer Applications 170(4):34-39, July 2017. BibTeX

	author = {Sakshi and Nirvair Neeru},
	title = {A Survey on different Compression Techniques for ECG Data Reduction},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {170},
	number = {4},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {34-39},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume170/number4/28062-2017914834},
	doi = {10.5120/ijca2017914834},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Electrocardiogram (ECG) is the technique that is used to record the electrical signal of the heart over a time interval by using the electrodes, positioned on a patient's body. The signals collected from the body needs to be processed and compressed before directing to monitoring center. Electrocardiogram (ECG) data compressions minimize the necessities of storage to generate a more proficient tele-cardiology system for the cardiac exploration and diagnosis. This paper focus on the evaluation of several compression schemes for ECG data compression and also provides the comparison of the various ECG compression techniques such as Turning Point, Delta Coding, AZTEC, CORTES, DCT etc. in terms of different performance metrics like Compression Ratio (CR), Percent Mean Square Difference (PRD) and Quality Score (QS).


  1. Hossein Mamaghanian,Nadia Khaled, David Atienza  and Pierre Vandergheynst, “Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes”, IEEE, transactions on Biomedical engineering, VOL. 58, NO. 9, Pp 2456-2466, 2011
  2. Xiaoxiao Wang, Zhijian Chen, Jiahui Luo, Jianyi Meng and Yin Xu, “ECG Compression based on combining of EMD and Wavelet Transform”, IEEE, Electronics Letters, Vol 52, Issue 9, Pp 1588-1590, 2016
  3. Jiali Ma, TanTan Zhang and MingChui Dong, “A Novel ECG data compression method using Adaptive Fourier decomposition with security guarantee in e-Health Applications”, IEEE, Journal of biomedical and health informatics, Vol 9, Issue 3, pp 986-994, 2015
  4. Yang He,Wenbin Yu,  Cailian Chen,  Yiyin Wang and  Xinping Guan, “Adaptive ECG Compression Scheme with prior knowledge support based on compressive sensing”, IEEE, Pp 1-5, 2015
  5. A. Bendifallah, R. Benzid and  M. Boulemden, “Improved ECG Compression Method using DCT”, IEEE, Electronics Letter, Vol 47, Issue 2, Pp 87-89, 2011
  6. Andrianiaina Ravelomanantsoa, Hassan Rabah and  Amar Rouane, “Simple and efficient compressed sensing encoder for WBAN”, IEEE, Transaction on instrument and Measurements, Vol 63, Issue 2, Pp 2973-2982, 2014
  7. Bo Yu, Liuqing Yang  and Chia-Chin Chong, “ECG Monitoring over Bluetooth: Data Compression and Transmission”, IEEE, Pp 1-5, 2010
  8. Zhe Liu, Wenbin Yu, Cailian Chen ,  Bo Yang and  Xinping Guan, “Adaptive Compression ratio estimation for categorified sparsity in real-time ECG Compression System”, IEEE, Pp 9439-9444, 2016
  9. Ziran Peng, Guojun Wang, Huabin Jiang and Shuangwu Meng, “Research and Improvement of ECG Compression algorithm based on EZW”, IEEE, Computer Methods and Programs in Biomedicine, Vol 145, Pp 157-166, 2017
  10. A. Singh,L.N. Sharma. and S. Dandapat, “Multichannel ECG data Compression using Compressed sensing in Eigen space”, IEEE, Computer in Biology and medicine, Vol 73, Issue 1, pp 24-37, 2016
  11. Sibasankar Padhy L.N. Sharma and S. Dandapat, “Multilead ECG data Compression using SVD in multiresolution Domain”, Biomedical Signal Processing and Control, Vol 23, Pp 10-18, 2016
  12. Giuliano Grossi, Raffaella Lanzarotti and Jianyi Lin, “High Rate Compression of ECG signals by an accuracy driven sparsity model relying on natural basis” Digital Signal Procession, Vol 45, pp 96-106, 2015
  13. Aymain Lbaida,Dhiah Al-Shammary and Ibrahim Khalil.,”Cloud enabled fractal based ECG compression on Wireless Body Sensor Networks”, Future generation Computer System, Vol 35, Pp 91-101, 2014
  14. Boqiang Huang, Yuanyuan Wang and Jianhua Chen, “ECG Compression using context modeling arithmetic coding with dynamic learning vector scalar quantization”, Biomedical Signal Processing and Control, Vol 8, issue 1, Pp 59-65, 2013
  15. Hong xin, Can-feng CHEN, Yan-ling WU, Pei-hua LI., “Decomposition and compression of ECG and EEG signals with sequence index coding methods based on matching pursuit”, The journal of china universities and telecommunication, Vol 19, Issue 2, Pp 92-95, 2012
  16. K. Ranjeet,A. Kuamr and Rajesh K. Pandey, “ECG signal Compression using optimum wavelet filters Bank Base on Kaiser Window”, Procedia Engineering, Vol 38, Pp 2889-2902, 2012
  17. Jianhua Chen,Fuyan Wang, Yufeng Zhang and Xinling Shi., “ECG Compression using uniform scalar dead zone quantization and conditional entropy coding”, Vol 30, Issue 4, Pp 523-530, 2008
  18. Xingyuan Wang and Juan Meng, “A 2-D ECG compression algorithm based on wavelet transform and vector quantization”, Digital Signal Processing, Vol 18, Issue 2, Pp 179-188, 2008.
  19. Anukul Pandey,Barjinder Singh Saini, Butta Singh and Neetu Sood, “A 2D electrocardiogram data compression method using a sample entropy based complexity sorting approach”,Computer and Electrical Engineering, Vol 56, Issue 10, pp 30-45, 2016
  20. A. Rabee and I. Barhumi, “ECG signal classification using support vector machine based on wavelet multiresolution analysis” IEEE, Vol 2, No 5 ,Pp 1319 – 1323, 2012
  21. Sung-Nien Yu  and Kuan-To Chou, “Combining Independent Component Analysis and Backpropagation Neural Network for ECG Beat Classification” IEEE, Pp 3090 – 3093, 2006
  22. M. Shahramand K. Nayebi, “Classification of multichannel ECG signals using a cross-distance analysis” IEEE, vol.3 , Pp 2182 - 2185 , 2011
  23. Butta Singh, Amandeep Kaur and Jugraj Singh, “A Review of ECG Data Compression Techniques”, International Journal of Computer Applications, Vol. 116, No. 11, Pp. 39-44, April 2015


ECG signal, Compressive Sensing, Time domain techniques, Wavelet based techniques.