A Hybrid Algorithm for Classification of Compressed ECG

Full Text (PDF, 301KB), PP.26-33

Views: 0 Downloads: 0


Shubhada S.Ardhapurkar 1,* Ramandra R. Manthalkar 2 Suhas S.Gajre 2

1. International Center of Excellence in Engineering and Management, Aurangabad, INDIA

2. S.G.G.S. Institute of Engineering and Technology, Nanded, Maharashtra, INDIA

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2012.02.04

Received: 28 Mar. 2011 / Revised: 3 Aug. 2011 / Accepted: 13 Oct. 2011 / Published: 8 Mar. 2012

Index Terms

Linear Predictive coding, Discrete wavelet transform, Probability Density Function


Efficient compression reduces memory requirement in long term recording and reduces power and time requirement in transmission. A new compression algorithm combining Linear Predictive coding (LPC) and Discrete Wavelet transform is proposed in this study. Our coding algorithm offers compression ratio above 85% for records of MIT-BIH compression database. The performance of algorithm is quantified by computing distortion measures like percentage root mean square difference (PRD), wavelet-based weighted PRD (WWPRD) and Wavelet energy based diagnostic distortion (WEDD). The PRD is found to be below 6 %, values of WWPRD and WEDD are less than 0.03. Classification of decompressed signals, by employing fuzzy c means method, is achieved with accuracy of 97%.

Cite This Paper

Shubhada S.Ardhapurkar, Ramandra R. Manthalkar, Suhas S.Gajre, "A Hybrid Algorithm for Classification of Compressed ECG", International Journal of Information Technology and Computer Science(IJITCS), vol.4, no.2, pp.26-33, 2012. DOI:10.5815/ijitcs.2012.02.04


[1]Yaniv Zigel, Arnon Cohen, and Amos Katz, ECG Signal Compression Using Analysis by Synthesis Coding [J]. IEEE Transactions on Biomedical Engineering, 2000, 47: 1308-1316.

[2]Gulay Tohumoglu, K. Erbil Sezgin, ECG signal compression by multi-iteration EZW coding for different wavelets and thresholds [J]. Computers in Biology and Medicine, Elsevier, 2007, 37: 173-182.

[3]R. Shantha Selva Kumari, V. Sadasivam, A novelalgorithm for wavelet based ECG signal coding [J]. Computers and Electrical Engineering, Elsevier, 2007, 33:186–194.

[4]Manuel Blanco-Velasco, Fernando Cruz-Roldán ,Juan Ignacio Godino-Llorente, and Kenneth E. Barner, Wavelet Packets Feasibility Study for the Design of an ECG Compressor[J]. IEEE Transactions On Biomedical Engineering, 2007,4 : 767-781.

[5]Justin Leo Cheang Loong, Khazaimatol S Subari, Rosli Besar and Muhammad Kamil Abdullah,A New Approach to ECG Biometric Systems: A Comparitive Study between LPC and WPD Systems [A]. World Academy of Science, Engineering and Technology, 2010, 68:759-764.

[6]K.I.Ramchandran, K.P.Soman, Insight into wavelets from theory to practice [M]. Prentice Hall Ltd, Second Edition, 2006.

[7]Ramchandra Manthalkar, Shubhada Ardhapurkar, Suhas Gajre,Wavelet Based ECG Denoising by Employing Cauchy Distribution at Subbands[C]. ICSP10 proceedings, 2010:1718-1721.

[8]Earl Gose, Richard Johnsonbaugh, Steve Jost, Pattern Recognition and Image Analysis[M]. Prentice Hall of India, 2005.

[9]Thomas Ledl, “Kernel Density Estimation: Theory and Application in Discriminant Analysis [J]. Austrian journal of statistics, 2004,43: 267-279.

[10]B.W. Silverman,Density Estimation For Statistics And Data Analysis[J]. Monographs on Statistics and Applied Probability, London: Chapman and Hall, 1986: 1-22.


[12]Gari Clifford Home page, Online Available: http://www.robots.ox.ac.uk/~gari/

[13Sabarimalai Manikandan, S. Dandapat, Wavelet energy based diagnostic distortion measure for ECG [J].Biomedical Signal Processing and Control, 2007:2 80–96.

[14]Rahime Ceylan, Yuksel Ozbay, Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network[J]. Expert System with Applications, ScienceDirect, 2007, 33: 286-295.