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Hybrid Compression Technique Using Linear Predictive Coding for Electrocardiogram Signals

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International Journal of Engineering Technology Science and Research IJETSR www.ijetsr.com ISSN 2394 – 3386 Volume 4, Issue 6 June 2017 Hybrid Compression Technique Using Linear Predictive Coding for Electrocardiogram Signals K. S Surekha B. P. Patil Research Scholar, Sinhgad College of Engg. Pune Principal (Assoc. Professor, AIT, Pune) AIT,Pune ABSTRACT Linear Predictive Coding (LPC) is used for analysis and compression of speech signals. Whereas Huffman coding is used forElectrocardiogram (ECG) signal compression. This paper presents a hybrid compression technique for ECG signal using modifiedHuffman encoding andLPC.The aim of this paper is to apply the linear prediction coding and modified Huffman coding for analysis, compression and prediction of ECG signals. The ECG signal is transformed through discrete wavelet transform. MIT-BIH database is used for testing the compression algorithm. The performance measure used to validate the results are the Compression ratio (CR) and Percent Root mean square difference (PRD).The improved CR and PRD obtained in this research prove the quality of the reconstructed signal. Key words Huffman encoding, Linear Predictive Coding, Compression 1. INTRODUCTION ECG gives clear indication about heart diseases and arrhythmias. It is necessary to monitor and store the ECG signal of subjects continuously for diagnosis purpose. Due to this, the data storage size keeps on increasing. In such situations, it is necessary to compress the ECG signal for storage and transmission. The important factor for consideration is to obtain maximum data compression. That must also ensure that the signal retain the clinically important features. Fig. 1 shows the ECG signal with different peaks. Fig. 1 The ECG signal with different peaks As shown in the fig. the ECG signal has different peaks. In this paper, a lossless hybrid technique of ECG signal is presented. The technique used is a hybrid technique consisting of LPC and Huffman encoding. In this paper, compression technique is explained in section 2. The Performance measure is briefed in Section 3. Section 4 gives an outline of the methodology used, Results and implementation. The last sections are of conclusion and references. 497 K. S Surekha, B. P. Patil International Journal of Engineering Technology Science and Research IJETSR www.ijetsr.com ISSN 2394 – 3386 Volume 4, Issue 6 June 2017 2. COMPRESSION TECHNIQUE The major classification of compression techniques is lossy and lossless. The lossy compression technique [1] can lose some part of the data. This paper gives importance to a lossless method of compression using Huffman and LPC.The lossless compression is preferred because of preserving the important features of ECGsignal which are required by doctors for diagnosis. Even the slightest loss of important part of the signal may lead to the wrong diagnosis. 2.1 Huffman encoding and Linear Predictive Coding Huffman coding is used as a compression technique with variable-length codes.The Huffman encoding algorithm initially arranges the symbols in descending order. This is done based on the probabalities.After this step, a binary tree is constructed. Every leaf of the tree includes a symbol. This procedure is carried out in steps. The codewords are obtained from the tree.In Huffman procedure, shorter code words are used for the symbols occurring frequently. In LPC, each expression sample is approximated as a combination of past samples. 2.2 The Wavelet Transform The wavelet transform is most suitable to display both the time and the frequency information together.The wavelet transform has two broad classifications. They are continuous wavelet transform (CWT) and Discrete Wavelet Transform (DWT)[2, 3]. In DWT, the wavelets are discretely sampled. The signal x is passed through low pass and high pass filter stages to obtain DWT of the signal.The convolution operation takes place through these filters simultaneously[4,5]. The corresponding output signal through low pass filter is given as below. y() n x k g n k (1) k A similar operation takes place with the high pass filter.[4,5]. In the next step, both the filter outputs go through the process of sub sampling by 2.The filtering and sub-sampling process are shown in fig. 2. Approximation Coefficients(output) g(n 2 ) x(n)(input) 2 Detail Coefficients(output) h(n) Fig. 2 Filtering and sub- sampling process in DWT The discrete wavelet transform is expressed as f() t Cm,, n m n t (2) 3 THE STANDARD PERFORMANCE MEASURE To check the validity of the results, different performance measures are used. In this research, Compression Ratio (CR) [6] and Percent Root mean square Difference (PRD) are used as standard measures of performance. The CR is defined as the ratio of a number of bits in the original signal to the number of bits in the compressed signal[7]. 498 K. S Surekha, B. P. Patil International Journal of Engineering Technology Science and Research IJETSR www.ijetsr.com ISSN 2394 – 3386 Volume 4, Issue 6 June 2017 4 METHODOLOGY USED , RESULTS AND IMPLEMENTATION MIT-BIH data base [8] is used for the testing purpose. The simulation is carried out using The MATLAB tool. The sampling frequency selected is 330 Hz. Initially, the ECG signal is passed through pre processing stage[9]. Base line wander noise and power line interference[4] are eliminated during this process by filtering process. The transformation of the signal is done through DWT. A threshold value is selected during this process. The coefficients below the threshold value are removed.The predictive coding algorithm is applied after the DWT stage. Once the coefficients are extracted through this step, the modified Huffman encoding step is applied for encoding of the samples. The results are tabulated for the different ECG signals as shown in Table 1. The results are also compared with the other researchers [ 9, 10] to prove the improvement in the CR and PRD. The low value of the PRD also indicates the good reconstruction quality of the signal. That helps in retaining the clinically significant properties of the ECG signal for diagnosis purpose. The waveforms showing both the input and output is shown in fig. 3.As shown in Table 1 the CR achieved by this method has shown a significant improvement by using the hybrid technique. Table 1: Result computation Method CR CR (%) PRD [10]Ranjeet Kumar et. al 6 1.11 [11],ShubhadeepBhaniket.al 50 Proposed 6.7 85 0.5 Input 500 0 amplitude/mv -500 0 20 40 60 80 100 120 140 Output after compression 500 0 amplitude/mv -500 0 20 40 60 80 100 120 140 sample no. Fig. 3 The Input signal and the output signal 5 CONCLUSION The methodology used in our research to make use of the hybrid technique shows the usefulness of the technique. The results also demonstrated improvement in the CR value and at the same time low PRD. The high value of CR is useful for better storage and transmission purpose. The low value of PRD achieved indicates good quality of the ECG signal. This will also help doctors for better diagnosis. The methodology used here is also based on the threshold value. In this research, the optimum value of the threshold value is selected to achieve high CR. 499 K. S Surekha, B. P. Patil International Journal of Engineering Technology Science and Research IJETSR www.ijetsr.com ISSN 2394 – 3386 Volume 4, Issue 6 June 2017 Acknowledgement We are grateful to the Principal SCOE(Sinhgad college of Engineering) HoD SCOE, PhD Coordinator SCOE who provided expertise that greatly assisted research to publish this paper. We are grateful the Director and Joint Director, Army Institute of Technology for providing support in publishing this paper. References [1] Mark Nelson, Jean Loup Gailly.”The Data Compression Book”,BPB, 2nd Edition,2009 [2] Mohammad Pooyan, Ali Taheri, MortezaMoazami-Goudarzi, ImanSaboori, “Wavelet Compression of ECG Signals using SPIHT Algorithm”, International Journal of Information & Communication Engineering, pp.219-225, 2005 [3] Mohammed Abo-Zahhad, Sabah M Ahmed, Ahmed Zakaria, “An Efficient Technique for Compressing ECG Signals Using QRS Detection, Estimation, and 2D DWT Coefficients Thresholding”, Modelling and Simulation in Engineering, Hindawi Publishing Corporation ), pp 1-10, 2012. [4] Surekha K S, B P Patil, “Compression of ECG Signal Using Hybrid Technique”, Intelligent Systems in Science and Information 2014, Springer International Publishing, pp.385-396,2014 [5] Surekha KS, BP Patil, “ECG Signal Compression Using the High Frequency Components of Wavelet Transform”, International Journal of Advanced Computer Science and Applications,Vol. 7, No. 3, 2016 [6] Yao Zau, Jun han, SizhongXuan, Shan Huang XinqianWeng, Debin Fang, XiaoyangZeng, “An energy efficient design for ECG recording and R peak detection based on wavelet Transform”, IEEE Transactions on circuits and systems, Vol. 62, No. 2, pp. 119-123, 2015 [7] Ktata, Sana Ouni, Kai's Ellouze, Nouredd. "A novel compression algorithm for electrocardiogram signals based on wavelet transform and SPIHT.”, International Journal of Signal Processing, 2009 [8] http://ecg.mit.edu/ [9] Sarita Mishra, Debasmit Das, Roshan Kumar, ParasuramanSumathi, “ A power line interference canceler based on sliding DFT phase locking scheme for ECG signals”, IEEE Ttransactions on Instrumentation and Measurement, Vol. 64, No. 1, pp 132-142, 2015 10] Ranjeet Kumar, A. Kumar, Rajesh K. Pandey, “Beta wavelet based ECG signal compression using lossless encoding with modified thresholding”, Journal of Computers in Biology and Medicine, Vol. 39, pp. 130-140, 2013 [11] ShubhadeepBanik, Roshan J. Martis2, Dayananda Nayak3,”Code Excited Linear Prediction Codec For Electrocardiogram”, Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco, 2004 500 K. S Surekha, B. P. Patil.
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