Efficient compression of bio-signals by using Tchebichef moments and Artificial Bee Colony

• Autorzy: Hosny K. M. , Khalid A. M. , Mohamed E. R.

Identyfikatory

DOI

10.1016/j.bbe.2018.02.006

• Języki publikacji: EN

Abstrakty

EN

In this paper, an algorithm is proposed for efficient compression of bio-signals based on discrete Tchebichef moments and Artificial Bee Colony (ABC). The Tchebichef moments are used to extract features of the bio-signals, then, the ABC algorithm is used to select of the optimum features which achieve the best bio-signal quality for a specific compression ratio (CR). The proposed algorithm has been tested by using different datasets of Electrocardio-gram (ECG), Electroencephalogram (EEG), and Electromyogram (EMG). The optimum feature selection using ABC significantly improve the quality of the reconstructed bio-signals. Different numerical experiments are performed to compress different records of ECG, EEG and EMG bio-signals by using the proposed algorithm and the most recent existing methods. The performance of the proposed algorithm and the other existing methods are evaluated using different metrics such as CR, PRD, and peak signal to noise ratio (PSNR). The comparison has shown that, at the same CR, the proposed compression algorithm yields the best quality of the reconstructed signals over the other existing methods.

Słowa kluczowe

EN

biosignals; electrocardiogram; electroencephalogram; electromyogram; Tchebichef moments; artificial bee colony

PL

biosygnały; elektrokardiogram; elektroencefalogram; elektromiogram; sztuczna kolonia pszczół

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 2
• Strony: 385--398
• Opis fizyczny: Bibliogr. 60 poz., rys., tab., wykr.

Twórcy

autor

Hosny K. M.

• Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt

autor

Khalid A. M.

• Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt

autor

Mohamed E. R.

• Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt

Bibliografia

• [1] Chang H-H, Moura JMF. Biomedical signal processing.2nd ed. Biomedical engineering and design handbook, vol. 1, 2nd ed. McGraw Hill; 2010. p. 559–79 [chapter 22].
• [2] Sahay S, Wadhwani AK, Wadhwani S. A survey approach on ECG feature extraction techniques. Int J Comput Appl 2015;120(June (11)). 0975–8887.
• [3] Naït-Ali A, Cavaro-Ménard C. Compression of biomedical images and signals. John Wiley & Sons, Inc.; 2007.
• [4] Umale C, Vaidya A, Shirude S, Raut A. Feature extraction techniques and classification algorithms for EEG signals to detect human stress – a review. Int J Comput Appl Technol Res 2016;5(1):8–14.
• [5] Ting W, Guo-zheng Y, Bang-hua Y, Hong S. EEG feature extraction based on wavelet packet decomposition for brain computer interface. Measurement 2008;41(6):618–25.
• [6] Reaz MBI, Hussain MS, Mohd-Yasin F. Techniques of EMG signal analysis: detection, processing, classification and applications. Biol Proced Online 2006;8(1):11–35.
• [7] Wootton R, Craig J, Patterson V. Introduction to telemedicine, vol. 206. London: Royal Society of Medicine Press; 2006.
• [8] Cinkler J, Kong X, Memon N. Lossless and near-lossless compression of EEG signals. Conference Record of the Thirty-First Asilomar Conference on Signals, Systems & Computers, vol. 2; 1997.
• [9] Koski A. Lossless ECG encoding. Comput Methods Programs Biomed 1997;52(1):23–33.
• [10] Mukhopadhyay SK, Mitra S, Mitra M. A lossless ECG data compression technique using ASCII character encoding. Comput Electr Eng 2011;37(4):486–97.
• [11] Batista LV, Melcher EUK, Carvalho LC. Compression of ECG signals by optimized quantization of discrete cosine transform coefficients. Med Eng Phys 2001;23(2):127–34.
• [12] Lukin V, Zriakhov M, Zelensky AA. Lossy compression of multichannel ECG based on 2-D DCT and pre-processing. 2008 Proceedings of International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science. IEEE; 2008. p. 159–62.
• [13] Aggarwal V, Patterh MS. Quality controlled ECG compression using Discrete Cosine Transform (DCT) and Laplacian Pyramid (LP). International, Multimedia, Signal Processing and Communication Technologies, 2009. IMPACT'09. IEEE; 2009. p. 12–5.
• [14] Shapiro JM. Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans Signal Process 1993;41 (12):3445–62.
• [15] Tohumoglu G, Sezgin KE. ECG signal compression by multi- iteration EZW coding for different wavelets and thresholds. Comput Biol Med 2007;37(2):173–82.
• [16] Pooyan T, Moazami-Goudarzi, Saboori. Wavelet compression of ECG signals using SPIHT algorithm. Int J Signal Process 2004;1(3).
• [17] Abo-Zahhad M, Ahmed SM, Zakaria A. An efficient technique for compressing ECG signals using QRS detection, estimation, and 2D DWT coefficients thresholding. Modelling and simulation in engineering, vol. 2012. 2012.
• [18] Peng Z, Wang G, Jiang H, Meng S. Research and improvement of ECG compression algorithm based on EZW. Comput Methods Programs Biomed 2017;145:157–66.
• [19] Huang B, Wang Y, Chen J. 2-D compression of ECG signals using ROI mask and conditional entropy coding. IEEE Trans Biomed Eng 2009;56(4):1261–3.
• [20] Kumar R, Kumar A, Pandey RK. Beta wavelet based ECG signal compression using lossless encoding with modified thresholding. Comput Electr Eng 2013;39(1):130–40.
• [21] Hung K-C, Wu T-C, Lee H-W, Liu T-K. EP-based wavelet coefficient quantization for linear distortion ECG data compression. Med Eng Phys 2014;36(7):809–21.
• [22] Kumar R, Kumar A, Singh GK. Hybrid method based on singular value decomposition and embedded zero tree wavelet technique for ECG signal compression. Comput Methods Programs Biomed 2016;129:135–48.
• [23] Fathi A, Faraji-kheirabadi F. ECG compression method based on adaptive quantization of main wavelet packet subbands. Signal Image Video Process 2016;10(8):1433–40.
• [24] Antoniol G, Tonella P. EEG data compression techniques. IEEE Trans Biomed Eng 1997;44(2):105–14.
• [25] Sriraam N. A high-performance lossless compression scheme for EEG signals using wavelet transform and neural network predictors. Int J Telemed Appl 2012;2012.
• [26] Srinivasan K, Dauwels J, Reddy MR. Multichannel EEG compression: wavelet-based image and volumetric coding approach. IEEE J Biomed Health Inf 2013;17(1):113–20.
• [27] Fathi A, Hejrati B, Abdali-Mohammadi F. Efficient lossless multi-channel EG compression based on channel clustering. Biomed Signal Process Control 2017;31:295–300.
• [28] Fathi A, Hejrati B, Abdali-Mohammadi F. A new near-lossless EEG compression method using ANN-based reconstruction technique. Comput Biol Med 2017;87:87–94.
• [29] Cárdenas-Barrera JL, Lorenzo-Ginori JV, Rodríguez-Valdivia E. A wavelet-packets based algorithm for EEG signal compression. Med Inform Internet Med 2004;29(1):15–27.
• [30] Higgins M, Walsh G, Jones. Lossy compression of EEG signals using SPIHT. Electron Lett 2011;47(18):1017–8.
• [31] Bazán-Prieto C, Blanco-Velasco M, Cárdenas-Barrera J, Cruz-Roldán F. Retained energy-based coding for EEG signals. Med Eng Phys 2012;34(7):892–9.
• [32] Daou H, Labeau F. Dynamic dictionary for combined EEG compression and seizure detection. IEEE J Biomed Health Inform 2014;18:247–56.
• [33] Maazouz, Kebir, Bengherbia, Toubal, Batel, Bahri. A DCT-based algorithm for multi-channel near-lossless EEG compression. 2015 4th International Conference on Electrical Engineering (ICEE). IEEE; 2015.
• [34] Norris, Englehart, Lovely. Steady-state and dynamic myoelectric signal compression using embedded zero-tree wavelets. Engineering in Medicine and Biology Society. Proceedings of the 23rd Annual International Conference of the IEEE, vol. 2; 2001.
• [35] Berger de A, Francisco, do Carmo, da Rocha. Compression of EMG signals with wavelet transform and artificial neural networks. Physiol Meas 2006;27(6):457.
• [36] Berger P de A, Nascimento FA de O, da Rocha AF. A new wavelet-based algorithm for compression of EMG signals. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. EMBS 2007. IEEE; 2007.
• [37] Eddie Filho BL, da Silva EAB, de Carvalho MB. On EMG signal compression with recurrent patterns. IEEE Trans Biomed Eng 2008;55(7):1920–3.
• [38] Costa, Berger, Rocha, Carvalho, Nascimento. Compression of electromyographic signals using image compression techniques. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2008. IEEE; 2008.
• [39] Pascal NE, Pierre E, Basile KI. Compression approach of EMG signal using 2D discrete wavelet and cosine transforms. Am J Signal Process 2013;3(1):10–6.
• [40] Trabuco MH, Costa MVC, Schwartz FP. Compression of S-EMG signals by transforms and spectral profile for bit allocation. 2013 Pan American Health Care Exchanges (PAHCE). IEEE; 2013.
• [41] Trabuco MH, Costa MVC, Nascimento FA de O. S-EMG signal compression based on domain transformation and spectral shape dynamic bit allocation. Biomed Eng Online 2014;13 (1):22.
• [42] Hosny KM. Exact Legendre moment computation for gray level images. Pattern Recognit 2007;40(12):3597–605.
• [43] Hosny KM. Robust template matching using orthogonal Legendre moment invariants. J Comput Sci 2010;6(10):1080–4.
• [44] Hosny KM, Papakostas GA, Koulouriotis DE. Accurate reconstruction of noisy medical images using orthogonal moments. 2013 18th International Conference on Digital Signal Processing (DSP). IEEE; 2013.
• [45] Zhu H, Liu M, Shu H, Zhang H, Luo L. General form for obtaining orthogonal moments. IET Image Process 2010;4:335–52.
• [46] Nakagaki K, Mukundan R. A fast 4-4 forward discrete Tchebichef transform algorithm. IEEE Signal Process Lett 2007;14(10):684–7.
• [47] Shu, Zhang, Chen, Haigron, Luo. Fast computation of Tchebichef moments for binary and grayscale images. IEEE Trans Image Process 2010;19(12):3171–80.
• [48] Abu NA, Wong SL, Herman NS, Mukundan R. An efficient compact Tchebichef moment for image compression. 2010 10th International Conference on Information Sciences Signal Processing and their Applications (ISSPA). IEEE; 2010.
• [49] Lazaridis P, Bizonpoulos A, Tzekis P, Zaharis Z, Debarge G, Gallion P. Comparative study of DCT and discrete Legendre transform for image compression. X International Conference on Electronics, Telecommunications, Automatics and Informatics (ETAI 2011); 2011.
• [50] Begum, Ragamathunisa AH, Manimegalai D, Abudhahir A. Optimum coefficients of discrete orthogonal Tchebichef moment transform to improve the performance of image compression. Malays J Comput Sci 2013;26(1):60–75.
• [51] Karaboga D. An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, vol. 200. Erciyes University, Engineering Faculty, Computer Engineering Department; 2005.
• [52] Karaboga, Gorkemli, Ozturk, Karaboga. A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 2014;42(1):21–57.
• [53] Bansal JC, Sharma H, Jadon SS. Artificial bee colony algorithm: a survey. Int J Adv Intell Paradig 2013;5(1–2):123–59.
• [54] Bolaji AL, Khader AT, Al-Betar MA, Awadallah MA. Artificial bee colony algorithm, its variants and applications: a survey. J Theor Appl Inf Technol 2013;47(2):434–59.
• [55] Brest, Greiner, Boskovic, Mernik, Zumer. Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 2006;10(6):646–57.
• [56] https://www.physionet.org/physiobank/database/mitdb/.
• [57] https://physionet.org/physiobank/database/edgiddb/.
• [58] https://physionet.org/pn6/chbmit/.
• [59] https://physionet.org/physiobank/database/emgdb/.
• [60] Daou H, Labeau F. EEG compression of scalp recordings based on dipole fitting. IEEE J Biomed Health Inform 2015;19 (3):995–1008.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).