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

Abstract 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 Electrocardiogram (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.

[1]  Dervis Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

[2]  D. Manimegalai,et al.  OPTIMUM COEFFICIENTS OF DISCRETE ORTHOGONAL TCHEBICHEF MOMENT TRANSFORM TO IMPROVE THE PERFORMANCE OF IMAGE COMPRESSION , 2013 .

[3]  Julián Cárdenas-Barrera,et al.  A wavelet-packets based algorithm for EEG signal compression , 2004, Medical informatics and the Internet in medicine.

[4]  Eduardo A. B. da Silva,et al.  On EMG Signal Compression With Recurrent Patterns , 2008, IEEE Transactions on Biomedical Engineering.

[5]  King-Chu Hung,et al.  EP-based wavelet coefficient quantization for linear distortion ECG data compression. , 2014, Medical engineering & physics.

[6]  Behzad Hejrati,et al.  A new near-lossless EEG compression method using ANN-based reconstruction technique , 2017, Comput. Biol. Medicine.

[7]  R. Mukundan,et al.  A Fast 4 $\times$ 4 Forward Discrete Tchebichef Transform Algorithm , 2007, IEEE Signal Processing Letters.

[8]  Jianhua Chen,et al.  2-D Compression of ECG Signals Using ROI Mask and Conditional Entropy Coding , 2009, IEEE Transactions on Biomedical Engineering.

[9]  N. Bahri,et al.  A DCT-based algorithm for multi-channel near-lossless EEG compression , 2015, 2015 4th International Conference on Electrical Engineering (ICEE).

[10]  Ramakrishnan Mukundan,et al.  An efficient compact Tchebichef Moment for image compression , 2010, 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010).

[11]  Gulay Tohumoglu,et al.  ECG signal compression by multi-iteration EZW coding for different wavelets and thresholds , 2007, Comput. Biol. Medicine.

[12]  Jerome M. Shapiro,et al.  Embedded image coding using zerotrees of wavelet coefficients , 1993, IEEE Trans. Signal Process..

[13]  Fabrice Labeau,et al.  EEG Compression of Scalp Recordings Based on Dipole Fitting , 2014, IEEE Journal of Biomedical and Health Informatics.

[14]  Anil Kumar,et al.  Beta wavelet based ECG signal compression using lossless encoding with modified thresholding , 2013, Comput. Electr. Eng..

[15]  Arnon D. Cohen,et al.  Biomedical Signal Processing , 1986 .

[16]  Abdolhossein Fathi,et al.  ECG compression method based on adaptive quantization of main wavelet packet subbands , 2016, Signal Image Video Process..

[17]  Behzad Hejrati,et al.  Efficient lossless multi-channel EEG compression based on channel clustering , 2017, Biomed. Signal Process. Control..

[18]  N. Sriraam,et al.  A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors , 2012, International journal of telemedicine and applications.

[19]  Joao L. A. Carvalho,et al.  A New Wavelet-Based Algorithm for Compression of Emg Signals , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  Ele Pierre,et al.  Compression Approach of EMG Signal Using 2D Discrete Wavelet and Cosine Transforms , 2013 .

[21]  Adson F da Rocha,et al.  Compression of EMG signals with wavelet transform and artificial neural networks , 2006, Physiological measurement.

[22]  Harish Sharma,et al.  Artificial bee colony algorithm: a survey , 2013, Int. J. Adv. Intell. Paradigms.

[23]  J. Cinkler,et al.  Lossless and near-lossless compression of EEG signals , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[24]  L. Batista,et al.  Compression of ECG signals by optimized quantization of discrete cosine transform coefficients. , 2001, Medical engineering & physics.

[25]  V. Lukin,et al.  Lossy compression of multichannel ECG based on 2-D DCT and pre-processing , 2008, 2008 International Conference on "Modern Problems of Radio Engineering, Telecommunications and Computer Science" (TCSET).

[26]  A Koski,et al.  Lossless ECG encoding. , 1997, Computer methods and programs in biomedicine.

[27]  Manjeet Singh Patterh,et al.  Quality controlled ECG compression using Discrete Cosine transform (DCT) and Laplacian Pyramid (LP) , 2009, 2009 International Multimedia, Signal Processing and Communication Technologies.

[28]  Yan Guozheng,et al.  EEG feature extraction based on wavelet packet decomposition for brain computer interface , 2008 .

[29]  M. Ramasubba Reddy,et al.  Multichannel EEG Compression: Wavelet-Based Image and Volumetric Coding Approach , 2013, IEEE Journal of Biomedical and Health Informatics.

[30]  Guojun Wang,et al.  Research and improvement of ECG compression algorithm based on EZW , 2017, Comput. Methods Programs Biomed..

[31]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[32]  Manuel Blanco-Velasco,et al.  Retained energy-based coding for EEG signals. , 2012, Medical engineering & physics.

[33]  Amine Nait-Ali,et al.  Compression of Biomedical Images and Signals , 2008 .

[34]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[35]  Dimitris E. Koulouriotis,et al.  Accurate reconstruction of noisy medical images using orthogonal moments , 2013, 2013 18th International Conference on Digital Signal Processing (DSP).

[36]  Khalid M. Hosny.  Robust Template Matching Using Orthogonal Legendre Moment Invariants , 2010 .

[37]  Kevin Englehart,et al.  Steady-state and dynamic myoelectric signal compression using embedded zero-tree wavelets , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[38]  E. Jones,et al.  Lossy compression of EEG signals using SPIHT , 2011 .

[39]  Pavlos I. Lazaridis,et al.  Comparative study of DCT and discrete Legendre transform for image compression , 2011 .

[40]  Fabrice Labeau,et al.  Dynamic Dictionary for Combined EEG Compression and Seizure Detection , 2014, IEEE Journal of Biomedical and Health Informatics.

[41]  Mohammed Azmi Al-Betar,et al.  Artificial bee colony algorithm, its variants and applications: A survey. , 2013 .

[42]  P. Tonella,et al.  EEG data compression techniques , 1997, IEEE Transactions on Biomedical Engineering.

[43]  A. K. Wadhwani,et al.  A Survey Approach on ECG Feature Extraction Techniques , 2015 .

[44]  Madhuchhanda Mitra,et al.  A lossless ECG data compression technique using ASCII character encoding , 2011, Comput. Electr. Eng..

[45]  Marcus V. C. Costa,et al.  Compression of electromyographic signals using image compression techniques , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[46]  Anil Kumar,et al.  Hybrid method based on singular value decomposition and embedded zero tree wavelet technique for ECG signal compression , 2016, Comput. Methods Programs Biomed..

[47]  Marcel Henrique Trabuco,et al.  S-EMG signal compression based on domain transformation and spectral shape dynamic bit allocation , 2014, BioMedical Engineering OnLine.

[48]  Mohammad Pooyan,et al.  Wavelet Compression of ECG Signals Using SPIHT Algorithm , 2007 .

[49]  R. Wootton,et al.  Introduction to Telemedicine , 1999 .

[50]  Mohammed Abo-Zahhad,et al.  An efficient technique for compressing ECG signals using QRS detection, estimation, and 2D DWT coefficients thresholding , 2012 .

[51]  Khalid M. Hosny,et al.  Exact Legendre moment computation for gray level images , 2007, Pattern Recognit..

[52]  F. P. Schwartz,et al.  Compression of S-EMG signals by transforms and spectral profile for bit allocation , 2013, 2013 Pan American Health Care Exchanges (PAHCE).

[53]  Huazhong Shu,et al.  Fast Computation of Tchebichef Moments for Binary and Grayscale Images , 2010, IEEE Transactions on Image Processing.