Robust ECG data compression method based on ε-insensitive Huber loss function

Abstract

Electrocardiogram (ECG) signals are continuously monitored for early diagnosis of heart diseases. However, a long-term monitoring generates large amounts of data at a level that makes storage and transmission difficult. Moreover, these records may be subject to different types of noise distributions resulting from operating conditions. Therefore, an effective and reliable data compression technique is needed for ECG data transmission, storage and analysis without losing the clinical information content. This study proposes the ε-insensitive Huber loss based support vector regression for the compressing of ECG signals. Since the Huber loss function is a mixture of quadratic and linear loss functions, it can properly take into account the different noise types in the data set. Compression performance of the proposed method has been assessed using ECG records from the MIT-BIH arrhythmia database. Experimental results demonstrate that the proposed loss function is an attractive candidate for compressing ECG data.

Keywords

• Data Compression,Electrocardiogram,Huber loss function,Support Vector Regression

References

1. [1] S. M. Jalaleddine, C. G. Hutchens, R. D. Strattan, and W. A. Coberly, “ECG data compression techniques-a unified approach,” IEEE Transsctions on Biomedical Engineering, vol. 37, no. 4, pp. 329-343, 1990.
2. [2] J. R. Cox, F. M. Nolle, H. A. Fozzard, and G. C. Oliver, “AZTEC, a preprocessing program for real-time ECG rhythm analysis,” IEEE Transactions on Biomedical Engineering, vol. 2, pp. 128-129, 1968.
3. [3] W. C. Mueller, “Arrhythmia detection program for an ambulatory ECG monitor. Biomedical sciences instrumentation,” vol. 14, pp. 81-85, 1978.
4. [4] J. P. Abenstein, and W. J. Tompkins, “A new data-reduction algorithm for real-time ECG analysis,” IEEE Transactions on Biomedical Engineering, vol. 1, pp. 43-48, 1982.
5. [5] G. Nave and A. Cohen, “ECG compression using long-term prediction,” IEEE transactions on Biomedical Engineering, vol. 40, no. 9, pp. 877-885, 1993.
6. [6] C. P. Mammen and B. Ramamurthi, “Vector quantization for compression of multichannel ECG,” IEEE Transactions on Biomedical Engineering, vol. 37, no. 9, pp. 821-825, 1990.
7. [7] C. Paggetti, M. Lusini, M. Varanini, A. Taddei, and C. Marchesi, “A multichannel template based data compression algorithm,” 1994 IEEE Conference on Computers in Cardiology, pp. 629-632, 1994.
8. [8] J. Ma, T. Zhang, and M. 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. 19, no. 3, pp. 986-994, 2015.

9. [9] M. Karczewicz and M. Gabbouj, “ECG data compression by spline approximation,” Signal Processing, vol. 59, no. 1, pp. 43-59, 1997.
10. [10] R. Benzid, A. Messaoudi, and A. Boussaad, “Constrained ECG compression algorithm using the block-based discrete cosine transform,” Digital Signal Processing, vol. 18, pp. 56-64, 2008.
11. [11] B. S. Reddy and I. S. N. Murthy, “ECG data compression using Fourier descriptors,” IEEE Transactions on Biomedical Engineering, vol. 4, pp. 428-434, 1986.
12. [12] P. S. Addison, “Wavelet transforms and the ECG: a review,” Physiological measurement, vol. 26, no. 5, pp. 155-199, 2005.
13. [13] M. S. Manikandan and 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.
14. [14] B. Singh, A. Kaur, and J. Singh, “A review of ecg data compression techniques,” International journal of computer applications, vol. 116, no. 11, pp. 39-44, 2015.
15. [15] A. A. Shinde and P. Kanjalkar, “The comparison of different transform based methods for ECG data compression,” 2011 IEEE International Conference on Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), pp, 332-335, 2011.
16. [16] O. Karal, “Destek Vektör Regresyon ile EKG Verilerinin Sıkıştırılması,” Journal of the Faculty of Engineering and Architecture of Gazi University (Accepted), 2018.
17. [17] O. Karal, “Maximum likelihood optimal and robust Support Vector Regression with lncosh loss function,” Neural networks, vol. 94, pp. 1-12, 2017.
18. [18] Z. Shi, and M. Han, “Support vector echo-state machine for chaotic time-series prediction,” IEEE Transactions on Neural Networks, vol. 18, no. 2, pp. 359-372, 2007.
19. [19] M. B. Huber, S. L. Lancianese, M. B. Nagarajan, I. Z. Ikpot, A. L. Lerner, and A Wismuller, “Prediction of biomechanical properties of trabecular bone in MR images with geometric features and support vector regression,” IEEE Transactions on Biomedical Engineering, vol. 58, pp. 1820-1826, 2011.
20. [20] H. Mahmoodian, L. Ebrahimian, “Using support vector regression in gene selection and fuzzy rule generation for relapse time prediction of breast cancer,” Biocybernetics and Biomedical Engineering, vol. 36, pp. 466-472, 2016.
21. [21] M. Valizadeh and M. R. Sohrabi, “The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 193, pp. 297-304, 2018.
22. [22] Y. Yaslan and B. Bican, “Empirical mode decomposition based denoising method with support vector regression for time series prediction: a case study for electricity load forecasting,” Measurement, vol. 103, pp. 52-61, 2017.
23. [23] N. Nava, T. D. Matteo, and T. Aste, “Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression,” Risks, vol. 6, no. 7, pp. 1-22, 2018.
24. [24] J. Massana, C. Pous, L. Burgas, J. Melendez, and J. Colomer, “Short-term load forecasting for non-residential buildings contrasting artificial occupancy attributes,” Energy and Buildings, vol. 130, pp. 519-531, 2016.
25. [25] A. Khosravi, R. N. N. Koury, L. Machado, and J. J. G. Pabon, “Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system,” Sustainable Energy Technologies and Assessments, vol.25, pp. 146-160, 2018.
26. [26] M. Guermoui, A. Rabehi, K. Gairaa, and S. Benkaciali, “Support vector regression methodology for estimating global solar radiation in Algeria,” The European Physical Journal Plus, vol. 133, no. 22, pp. 1-9, 2018.
27. [27] U. K. Das, K. S. Tey, M. Seyedmahmoudian, S. Mekhilef, M. Y. I. Idris, W. Van Deventer, and A. Stojcevski, “Forecasting of photovoltaic power generation and model optimization: A review,” Renewable and Sustainable Energy Reviews, vol. 81, pp. 912-928, 2018.
28. [28] B. Schölkopf and A. J. Smola, “Learning with kernels: support vector machines, regularization, optimization, and beyond,” MIT press, 2002.
29. [29] G. B. Moody and R. G. Mark, “The impact of the MIT-BIH arrhythmia database,” IEEE Engineering in Medicine and Biology Magazine, vol. 20, pp. 45-50, 2001.