- Research Article
- Open Access
Lossless Compression Schemes for ECG Signals Using Neural Network Predictors
EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 035641 (2007)
This paper presents lossless compression schemes for ECG signals based on neural network predictors and entropy encoders. Decorrelation is achieved by nonlinear prediction in the first stage and encoding of the residues is done by using lossless entropy encoders in the second stage. Different types of lossless encoders, such as Huffman, arithmetic, and runlength encoders, are used. The performances of the proposed neural network predictor-based compression schemes are evaluated using standard distortion and compression efficiency measures. Selected records from MIT-BIH arrhythmia database are used for performance evaluation. The proposed compression schemes are compared with linear predictor-based compression schemes and it is shown that about 11% improvement in compression efficiency can be achieved for neural network predictor-based schemes with the same quality and similar setup. They are also compared with other known ECG compression methods and the experimental results show that superior performances in terms of the distortion parameters of the reconstructed signals can be achieved with the proposed schemes.
Jalaleddine SMS, Hutchens CG, Strattan RD, Coberly WA: ECG data compression techniques—a unified approach. IEEE Transactions on Biomedical Engineering 1990,37(4):329-343. 10.1109/10.52340
Stearns SD, Tan L-Z, Magotra N: Lossless compression of waveform data for efficient storage and transmission. IEEE Transactions on Geoscience and Remote Sensing 1993,31(3):645-654. 10.1109/36.225531
Dony RD, Haykin S: Neural network approaches to image compression. Proceedings of the IEEE 1995,83(2):288-303. 10.1109/5.364461
Rizvi SA, Wang L-C, Nasrabadi NM: Neural network architectures for vector prediction. Proceedings of the IEEE 1996,84(10):1513-1528. 10.1109/5.537115
Logeswaran R, Eswaran C: Performance survey of several lossless compression algorithms for telemetry applications. International Journal of Computers and Applications 2001,23(1):1-9.
Sayood K: Introduction to Data Compression. 3rd edition. Morgan Kaufmann, San Francisco, Calif, USA; 2006.
Aydin MC, Cetin AE, Koymen H: ECG data compression by sub-band coding. Electronics Letters 1991,27(4):359-360. 10.1049/el:19910227
Tai SC: Six-band sub-band coder on ECG waveforms. Medical & Biological Engineering & Computing 1992,30(2):187-192.
Istepanian RSH, Hadjileontiadis LJ, Panas SM: ECG data compression using wavelets and higher order statistics methods. IEEE Transactions on Information Technology in Biomedicine 2001,5(2):108-115. 10.1109/4233.924801
Lee H, Buckley KM: ECG data compression using cut and align beats approach and 2-D transforms. IEEE Transactions on Biomedical Engineering 1999,46(5):556-564. 10.1109/10.759056
Rajoub BA: An efficient coding algorithm for the compression of ECG signals using the wavelet transform. IEEE Transactions on Biomedical Engineering 2002,49(4):355-362. 10.1109/10.991163
Alshamali A, Al-Fahoum AS: Comments on "An efficient coding algorithm for the compression of ECG signals using the wavelet transform". IEEE Transactions on Biomedical Engineering 2003,50(8):1034-1037. 10.1109/TBME.2003.814531
Djohan A, Nguyen TQ, Tompkins WJ: ECG compression using discrete symmetric wavelet transform. Proceedings of the 17th IEEE Annual Conference of Engineering in Medicine and Biology (IEMBS '95), September 1995, Montreal, Que, Canada 1: 167–168.
Cârdenas-Barrera JL, Lorenzo-Ginori JV: Mean-shape vector quantizer for ECG signal compression. IEEE Transactions on Biomedical Engineering 1999,46(1):62-70. 10.1109/10.736756
Miaou S-G, Yen H-L, Lin C-L: Wavelet-based ECG compression using dynamic vector quantization with tree codevectors in single codebook. IEEE Transactions on Biomedical Engineering 2002,49(7):671-680. 10.1109/TBME.2002.1010850
Koski A: Lossless ECG encoding. Computer Methods and Programs in Biomedicine 1997,52(1):23-33. 10.1016/S0169-2607(96)01779-8
Giurcǎneanu CD, Tǎbuş I, Mereuţǎ Ş: Using contexts and R-R interval estimation in lossless ECG compression. Computer Methods and Programs in Biomedicine 2002,67(3):177-186. 10.1016/S0169-2607(01)00126-2
Stearns SD: Arithmetic coding in lossless waveform compression. IEEE Transactions on Signal Processing 1995,43(8):1874-1879. 10.1109/78.403346
Witten IH, Neal RM, Cleary JG: Arithmetic coding for data compression. Communications of the ACM 1987,30(6):520-540. 10.1145/214762.214771
Moffat A, Neal RM, Witten IH: Arithmetic coding revisited. ACM Transactions on Information Systems 1998,16(3):256-294. 10.1145/290159.290162
Moody GB: MIT-BIH Arrhythmia Database CD-ROM. 3rd edition. Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass, USA; 1997.
Hagan MT, Menhaj MB: Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 1994,5(6):989-993. 10.1109/72.329697
Hagan MT, Demuth HB, Beale M: Neural Network Design. Thomson Learning, Boston, Mass, USA; 1996.
Zigel Y, Cohen A, Katz A: The weighted diagnostic distortion (WDD) measure for ECG signal compression. IEEE Transactions on Biomedical Engineering 2000,47(11):1422-1430. 10.1109/TBME.2000.880093
Ishijima M: Fundamentals of the decision of optimum factors in the ECG data compression. IEICE Transactions on Information and Systems 1993,E76-D(12):1398-1403.
Al-Fahoum AS: Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure. IEEE Transactions on Information Technology in Biomedicine 2006,10(1):182-191. 10.1109/TITB.2005.855554
Wei J-J, Chang C-J, Chou N-K, Jan G-J: ECG data compression using truncated singular value decomposition. IEEE Transactions on Information Technology in Biomedicine 2001,5(4):290-299. 10.1109/4233.966104
Bradie B: Wavelet packet-based compression of single lead ECG. IEEE Transactions on Biomedical Engineering 1996,43(5):493-501. 10.1109/10.488797
Benzid R, Marir F, Boussaad A, Benyoucef M, Arar D: Fixed percentage of wavelet coefficients to be zeroed for ECG compression. Electronics Letters 2003,39(11):830-831. 10.1049/el:20030560
Lu Z, Kim DY, Pearlman WA: Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm. IEEE Transactions on Biomedical Engineering 2000,47(7):849-856. 10.1109/10.846678
Hilton ML: Wavelet and wavelet packet compression of electrocardiograms. IEEE Transactions on Biomedical Engineering 1997,44(5):394-402. 10.1109/10.568915
Zigel Y, Cohen A, Katz A: ECG signal compression using analysis by synthesis coding. IEEE Transactions on Biomedical Engineering 2000,47(10):1308-1316. 10.1109/10.871403
Shannon CE: A mathematical theory of communication. Bell System Technical Journal 1948, 27: 379–423.
Gallagher RG: Variations on a theme by Huffman. IEEE Transactions on Information Theory 1978,24(6):668-674. 10.1109/TIT.1978.1055959
Sayed AH: Fundamentals of Adaptive Filtering. John Wiley & Sons, New York, NY, USA; 2003.
Haykin S: Adaptive Filter Theory. 4th edition. Prentice-Hall, Upper Saddle River, NJ, USA; 2002.
About this article
Cite this article
Kannan, R., Eswaran, C. Lossless Compression Schemes for ECG Signals Using Neural Network Predictors. EURASIP J. Adv. Signal Process. 2007, 035641 (2007). https://doi.org/10.1155/2007/35641
- Neural Network
- Quantum Information
- Efficiency Measure
- Compression Method
- Reconstructed Signal