Abstract

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).

References

• 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
• 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
• 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
• 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
• A. Bendifallah, R. Benzid and  M. Boulemden, “Improved ECG Compression Method using DCT”, IEEE, Electronics Letter, Vol 47, Issue 2, Pp 87-89, 2011
• 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
• Bo Yu, Liuqing Yang  and Chia-Chin Chong, “ECG Monitoring over Bluetooth: Data Compression and Transmission”, IEEE, Pp 1-5, 2010
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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.
• 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
• 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
• Sung-Nien Yu  and Kuan-To Chou, “Combining Independent Component Analysis and Backpropagation Neural Network for ECG Beat Classification” IEEE, Pp 3090 – 3093, 2006
• M. Shahramand K. Nayebi, “Classification of multichannel ECG signals using a cross-distance analysis” IEEE, vol.3 , Pp 2182 - 2185 , 2011
• 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