CHAPTER 1 INTRODUCTION

Transcription

1 CHAPTER 1 INTRODUCTION 1.1 Introduction and Motivation One of the most vital and intuitive test for the present condition of the patient is electrocardiogram (ECG) record graphically, which depicts the electrical activity of the heart in a human body. The heart activity of a human being produces electrical waves which are measured as ECG signal. The ECG signal is the measure of the bioelectrical signal produced at the surface of human body due the beating of the heart. This signal depicts extremely important information to the doctors, as it provides patient s cardiac condition at the moment [Velasco et al., 2007]. As most of the hospitals are going for the electronic patient record (EPR) of the patients, data compression has become a big issue in the world of biomedical electronics, as the need of more and more record maintenance of the patients is required without saturation of the available storage. Besides this, recording the patient s cardiac data in the digital format with appropriate accuracy so that even minor changes occurring in the ECG record can be traced is of great concern in the field of biomedical signal processing. For example, a 3 channel, 24 hour ambulatory ECG typically has storage requirement of over 50 MB. Thus, there is need to compress the data effectively to reduce the storage cost and convenient transmission of the record over telephone lines and other channels which have limited channel capacity. This requires an effective data compression technique [Pooyan et al., 2004]. The main aim of any compression technique is to achieve maximum data volume reduction while preserving the significant signal morphology features upon reconstruction [Pooyan et al., 2005]. One of the primary aims of ECG data compression is to achieve maximum compression of the data without any loss of the diagnostic features of the signal. Data compression methods can be classified into two main families: lossless and Lossy methods. Methods 1

2 from the lossless family can obtain an exact reconstruction of the original signal but low data rates cannot be achieved through these methods. In contrast, Lossy methods do not obtain an exact reconstruction, but higher compression can be obtained [Alesanco and Garcia, 2008]. The commonly used ECG compression techniques are Lossy in nature. The techniques fall into three categories [Jalaleddine et al., 1990] [Chen and Itoh, 1998]: (i) Direct methods, in which actual signal samples are analyzed (time domain) [Pooyan et al., 2004]. Direct compression such as Amplitude-Zone-Time Epoch Coding (AZTEC) method, modified AZTEC, the coordinate reduction time coding system (CORTES), turning point (TP) technique, Scan-Along Polygonal Approximation (SAPA), fan, delta code algorithm, peak-picking, cycle-to-cycle, differential pulse code modulation (DPCM) and the long-term prediction (LTP) [Nave and Cohen, 1993][Philips, 1993]. Most of the above mentioned techniques are highly efficient in data compression but due to the inability of exact reconstruction like actual signal and discontinuities occurring, the results are unacceptable to the cardiologists. However, a significant reduction of such discontinuities is achieved by using a smoothing parabolic filter at acceptable limit of amplitude distortion to the ECG [Saxena et al., 2007]. (ii) Transformational methods, in which first signal is transformed in suitable domain and then spectral and energy distribution analysis of the signal is carried out [Pooyan et al., 2004]. Some of the transformations used in transformational compression methods are Fourier transform (FT), Haar transform (HT), Walsh Transform, Karhunen-Loeve Transform (KLT), discrete cosine transform (DCT) [Pooyan et al., 2004], the optimally Warped transform sub-band coding and Wavelet Transform (WT) [Saxena et al., 2007]. Linear transformations like FT, cosine transform (CT), and Walsh Transform are applied to the signal, and then compression via redundant sample reduction is applied in the transform domain rather than in the time domain. Typically, the transformation process 2

3 produces a sequence of coefficients, which reduce the amount of data required to adequately represent the original signal. Out of all the transformative techniques, the highest compression ratio (CR) for multilead ECG data has been reported for KLT technique. Moreover, the KLT results in de-correlated transform coefficients and minimizes the total entropy compared to the other transforms. The computation time needed to calculate the KLT basis vectors is very intense, which has given a chance for the use of sub-optimal transforms with fast algorithms like FT, CT and HT [Saxena et al., 2007]. (iii) Parameter extraction method is an irreversible process with which a particular characteristics or parameter of the signal can be extracted. The extracted parameters (e.g. measurement of the probability distribution) are subsequently utilized for classification based on a prior knowledge of the signal features [Jalaleddine et al., 1990]. A preprocessor is employed to extract features of the ECG signal and the same is later used to reconstruct the signal. Peak peaking, linear prediction methods, syntactic, cycle pool based compression (CPBC) and neural nets are the methods in this category. In almost all compression methods, a procedure is involved to select the line segments, slope segments, segment lengths, amplitude of segment extreme points, setting of error thresholds and coding schemes. Again a procedure is involved to decode information stored in some coded form to reconstruct the signal [Saxena et al., 2007]. In most cases, the direct methods are good as these are simple and less erroneous but provide low compression ratio (CR). The transform based methods produce better compression results but these are Lossy method. In spite of being Lossy in nature these methods are preferred because of the large CR provided by them [Goudarzi and Moradi, 2005]. The current interest of researchers is to improve the performance of these methods. 3

4 The work in this thesis is also focused on developing better and tunable transformation based ECG compression methods. A lot of work has been carried out to compare various compression methods. But as such, the comparison is a difficult job due to the following reasons: (i) Different algorithms have been evaluated on different databases (ii) Various types of errors have been used to express the dissimilarities between the original and reconstructed signal, and (iii) Various types of compression criteria have been used to evaluate performance [Saxena et al., 2007]. The criteria for evaluation of the efficiency of a particular method depend on two factors: (i) the amount of compression and (ii) the resultant reconstruction error. Several difficulties exist in the definition of these factors [Saxena et al., 2007]. The aim in compression is to remove all the information that bears no diagnostic content. The definition of error can then be made in terms of the reconstructed signal. This has been termed as Diagnostability. The only way to measure diagnostability is to carry out survey with a relatively large number of cardiologists who evaluate strips of reconstructed signals and grade them as per their expert opinion. This is, of course, impractical, as it would be near impossible to get such an evaluation done in most of the cases. Such criterions are not available in the literature also [Saxena et al., 2007]. 1.2 Literature Review In the last few decades, numerous algorithms have been developed for the ECG data compression. However, methods and independent databases to test the reliability of such programs are still scarce. Earlier work was based on comparing the number of samples in the original data with the resulting compression parameters without taking in to account factors such as sampling frequency, bandwidth, word-length of compression parameters, 4

5 and precision of original data, database size, lead selection and noise level. A detailed literature survey has been carried out in line with the titled work to find out the current level of research. The main aim of the study was to develop quality control ECG compression technique. Furht and Perez in [Furht and Perez, 1988] developed a real-time compression algorithm in which a modification of the amplitude zone time epoch coding (AZTEC) technique extended with several statistical parameters used to calculate the variable threshold. The algorithm applied in the design of a pacemaker follow up system for the on-line ECG data transmission. S.M.S. Jalaleddine et al. in [Jalaleddine et al., 1990] proposed a unified view of ECG compression techniques as direct data compression and transformation method. The direct data compression techniques were: ECG differential pulse code modulation, entropy coding, AZTEC, Turning-point, CORTES, Fan, SAPA algorithms, peak picking, and cycle-to-cycle compression methods. The transformation methods were: Fourier transform, Walsh transform and K-L transform. The direct ECG data compression schemes were presented and classified into: tolerance-comparison compression, DPCM, and entropy coding methods. G. Einarsson in [Einarsson, 1991] presented an algorithm for reversible data compression based on predictive coding. From the input data, a sequence of integer-valued residuals was generated by a linear or nonlinear operation. The size of the alphabet for the residuals was reduced by performing a modular operation on its symbols. The modular operation resulted in a smaller size codebook and prevents data expansion when the source was not matched to the code. It reduces the entropy of the residuals, which theoretically should result in a higher degree of data compression, but are of little practical significance. 5

6 Nave and Cohen in [Nave and Cohen, 1993] introduced ECG signal compression based on the sub-auto regression (SAR) model, known as the long-term prediction (LTP) model. The periodicity of the ECG signal was in order to further reduce redundancy, thus yielding higher compression ratios. W. Philips in [Philips, 1993] observed an adaptive compression method for ECG signal in which each R-R interval was an optimally time-warped polynomial. It achieves a highquality approximation at less than 250 bits/s. The method was less sensitive to errors in QRS detection and it removes more (white) noise from the signal. K. Anant et al. in [Anant et al., 1995] improved wavelet compression algorithm for ECG signals with the use of vector quantization on wavelet coefficients. Vector quantization on scales of long duration and low dynamic range retains feature integrity of the ECG with a very low bit-per-sample rate. Results indicate that the proposed method excels over standard techniques S.M.S. Jalaleddine et al. in [Jalaleddine et al., 1990] for high fidelity compression. K. Nagarajan et al. in [Nagarajan et al., 1996] introduced a constraint on PRD and used wavelet packet decomposition. The constraint was in the form of upper bound. This bound was based on the initial performance of the algorithm and could be specified by the clinician after correlating the quality of the compressed versions of the ECG and the resulting PRD. Chen and Itoh in [Chen and Itoh, 1998] presented a new ECG compression method based on orthonormal wavelet transform and an adaptive quantization strategy, by which a predetermined percent root mean square difference (PRD) can be guaranteed with high compression ratio and low implementation complexity. 6

7 Z. Lu et al. in [Lu et al., 2000] proposed a wavelet ECG data codec based on the Set Partitioning in Hierarchical Trees (SPIHT) compression algorithm. They modified the algorithm for the one-dimensional (1-D) case and applied it to compression of ECG data. Istepanian and Petrosian in [Istepanian and Petrosian, 2000] presented an optimal zonal wavelet-based ECG data compression (OZWC) method for a mobile telecardiology system. This optimal wavelet algorithm achieved a compression ratio of 18:1 with low PRD ratios. Y. Zigel et al. in [Zigel et al., 2000 (b)] introduced a new distortion measure for ECG signal compression, called weighted diagnostic distortion (WDD). The WDD was based on PQRST complex diagnostic features of the original ECG signal and the reconstructed one. Four compression algorithms were implemented (AZTEC, SAPA2, LTP, ASEC) to evaluate the WDD. A mean opinion score (MOS) test was applied to test the quality of the reconstructed signals and compare the quality measure (MOSerror) with the WDD measure and PRD measure. R.S.H. Istepanian et al. in [Istepanian et al., 2001] evaluated the compression performance and characteristics of two wavelet coding compression schemes i.e. optimal zonal wavelet coding (OZWC) method, Istepanian and Petrosian in [Istepanian and Petrosian, 2000] and the wavelet transform higher order statistics-based coding (WHOSC) method of ECG signals. The WHOSC method employs higher order statistics (HOS) and uses multirate processing with the autoregressive HOS model technique to provide increasing robustness to the coding scheme. R. Benzid et al. in [Benzid et al., 2003] presented a new method for ECG compression. After the pyramidal wavelet decomposition, the resultant coefficients are subjected to an iterative threshold until a fixed percentage target of wavelet coefficients to be zeroed was reached. Next, the lossless Huffman s coding was used to increase the compression ratio. 7

8 M. Pooyan et al in [Pooyan et al., 2004] presented an approach for wavelet compression of ECG signals based on the set partitioning in hierarchical trees (SPIHT) coding algorithm. The results show the high efficiency of this method in ECG compression. Goudarzi and Moradi in [Goudarzi and Moradi, 2005] found the optimum multiwavelet for compression of ECG signals. The different multiwavelets were applied in ECG compression. The known factors were then calculated, such as Compression Ratio (CR), Percent Root Difference (PRD), Distortion (D), and Root Mean Square Error (RMSE) from compression literature. They employed the Cross Correlation (CC) criterion and Signal to Noise Ratio (SNR). R. Benzid et al. in [Benzid et al., 2006] presented a quality-controlled compression method in which ECG signal was decomposed using the wavelet transform. The resulting coefficients were thresholded iteratively until a pre-defined percent root mean square difference (PRD) is matched. The quantization strategy of extracted non-zero wavelet coefficients (NZWC), according to the combination of RLE, HUFFMAN and arithmetic encoding of the NZWC and a resulting lookup table, allow high compression ratios with good quality reconstructed signals. B.S. Kim et al. in [Kim et al., 2006] proposed a wavelet-based ECG compression algorithm with a low delay. The algorithm reduces the frame size as much as possible to achieve a low delay, while maintaining reconstructed signal quality. To attain both low delay and high quality, it employs waveform partitioning, adaptive frame size adjustment, wavelet compression, flexible bit allocation, and header compression. M.B. Velasco et al. in [Velasco et al., 2007] presented a thresholding-based method to encode ECG signals using WP and the results were compared with DWT as reported by R. Benzid et al. in [Benzid et al., 2003]. 8

9 Z. Arnavut in [Arnavut, 2007] showed that compressing ECG signals, utilization of linear prediction, Burrows-wheeler transformation, and inversion ranks yield better compression gain in terms of weighted average bit per sample than ECG-specific coders. R. Benzid et al. in [Benzid et al., 2007] proposed an ECG compression based on pyramidal digital wavelet transform. The resulting wavelet coefficients were thresholded iteratively until a user-specified percentage root-mean-square difference is matched. Then the non-zero coefficients of the threshold vector were quantized adaptively by the linear quantizer of the lowest possible resolution. In the last step, the quantized wavelet coefficients vector was stored efficiently by a two-role encoder. Hossain and Amin in [Hossain and Amin, 2011] described an efficient ECG signal compression technique based on the combination of wavelet transform and thresholding of the wavelet coefficients according to their energy compaction properties in different sub bands to achieve high CR with low PRD. First, the ECG signal was wavelet transformed using different discrete wavelets. The wavelet transform was based on dyadic scales and decomposes the ECG signals into five detailed band levels and one approximation band level. Then, the wavelet coefficients in each subbands are thresholded using a threshold based on energy packing efficiency (EPE) of the wavelet coefficients. Table 1.1 presents the performance comparison of important ECG compression techniques. From this table it is observed that the CR ranges from 2 to 92 and PRD from 0.57 to 28%. It means only CR and PRD do not give proper scale to compare. Therefore, most convincing way to evaluate the performance of compression technique is to see whether the clinical information is being retained or not [Giri, 2003]. 9

10 Table 1.1: Performance comparison of ECG compression techniques Method CR PRD(%) AZTEC [Jalaleddine et al., 1990] TP [Jalaleddine et al., 1990] CORTES [Jalaleddine et al., 1990] FAN/SAPA [Jalaleddine et al., 1990] 3 4 Peak-Picking (Spline) with entropy Encoding [Jalaleddine et al., 1990] DPCM Linear Predict, interpolation and entropy coding [Jalaleddine et al., 1990] Orthogonal transform -CT,KLT,HT [Jalaleddine et al., 1990] 3 - Dual application of K-L transformation [Jalaleddine et al., 1990] 12 - Fourier Descriptors [[Jalaleddine et al., 1990] Truncated Singular Value Decomposition (TSVD) [Wei et al., 2001] Multiwavelets [Goudarzi and Moradi, 2005] Fixed percentage of wavelet coefficients to be zeroed [Benzid et al., 2003] Wavelet packets [Velasco et al., 2007] OZWC [Istepanian and Petrosian, 2000] SPIHT [Lu et al., 2000] Video Codec technology [Chen and Yang, 2008] Subband Thresholding of the Wavelet Coefficients [Hossain and Amin, 2011] Wavelet Transfrom [Charles and Prasad, 2011] Discrete Wavelet Transfrom [Shakya and Wadhwani, 2012] 92 >1% 10

11 1.3 Research Gaps The review of available literature reveals that different algorithms have been evaluated on different database. Various types of compression criteria and errors have been used to express the dissimilarities between the original and reconstructed signal. Compression schemes are classified into three categories: parameter extraction method, direct method and transform method. Parameter extraction method is an irreversible process with which a particular parameter is extracted from the signal. Direct methods process and code the signals in a time domain. Transformation based methods process and code signals in the transformed domain. Existing data compression techniques are based on Direct or Transform based methods [Jalaleddine et al., 1990]. In most cases, direct methods are superior to transform methods with respect to simplicity and error. However, transform methods achieve higher compression rates and are insensitive to noise contained in original ECG signal [Goudarzi and Moradi, 2005]. Research work in this thesis will be focused on transformation based ECG signal compression methods because these methods provides better CR as compared to Direct method. The literature review reveals that more stress is laid on the development of simple compression schemes, in terms of computation, that produces high-quality reconstruction. A number of methods have been developed for ECG data compression, but still no method can be claimed to reach a state of perfection to deal with all types of ECG signals. There is ample scope to make lot of improvement in the existing transformation based ECG compression techniques and to develop new, more efficient and effective and utilizing different transformation techniques that provides higher CR without significant loss of information. Therefore, there is a need to extend the research study in the transformation based ECG compression techniques. 11

12 1.4 Objectives of Research Work The main objective of research work is to develop an efficient and tunable transformation based ECG compression technique. To accomplish the main objective first the transformation based ECG compression techniques will be studied and the possibility of improvement in these will be explored. Second the various transforms that are not used till date for ECG compression will be studied for their suitability for transformation based ECG compression techniques. 1.5 Thesis Organization This section presents the organization of the thesis. Chapter 1 introduces the concept of ECG compression and motivation to carry out the work. The literature review, objectives of research, thesis organization and contributions are also presented in this chapter. Chapter 2 deals with description of ECG signal characteristics, importance of ECG compression. Also, the linear transform techniques (i.e Discrete Cosine Transform (DCT), Cosine Packet Transform (CPT), Laplacian pyramid Transform (LPT), Slantlet Transform (SLT), Wave Atom Transform (WAT), Wavelet Transform (WT), Wavelet Packet Transform (WPT)), and Alpert Multiwavelet Transform (AMW), quantizer (Max Lloyd) and encoder (Huffman coding and Arithmetic coding) are described in this chapter. Chapter 3 covers the concept of methodology for quality controlled ECG compression based on linear transforms. The effect of normalization is also studied. Then another method Genetic Algorithm (GA) is used to calculate the optimum value of threshold. These techniques are analyzed and discussed based on the results obtained. Chapter 4 represents the analysis of non linear transforms (i.e Essentially Non-Oscillatory Point-Value (ENOPV) Transform, Essentially Non-Oscillatory Cell-Average (ENOCA) Transform, Maxlift Transform, Medlift Transform and Lifting Wavelet Transform (LWT)) 12

13 based quality controlled ECG compression along with its mathematical background. The effect of normalization on LWT is also studied. Then results are discussed and compared with the results of linear transform based methods. Finally, the overall thesis summary is concluded in chapter 5 along with its other objectives that can be attained for future work. 1.6 Contributions Many researcher s interest is based on quality controlled ECG compression technique. The main stress in this thesis is therefore laid on quality controlled ECG compression via transform based methods so as to achieve high CR. The main contributions of the research work done are: Improving CR at low PRD by using Wave Atom Transform (WAT) based ECG compression method. To avoid discontinuities in the ECG signal by using Essentially Non Oscillatory (ENO) techniques which leads to lesser number of coefficients at the edges and thus improves the CR. To get better CR at high PRD by using Lifting wavelet transform (LWT) based ECG compression method. Including the normalization step before thresholding which substantially increases the CR. To get optimum value of threshold by using Genetic Algorithm (GA) method. To the best of our knowledge to date above methods has not been used by any researcher s for ECG compression. Most of the researchers have used linear transform techniques DCT, WT and WPT for quality controlled ECG compression. Besides these linear transform techniques, there are other linear transform techniques such as CPT, LPT, SLT, WAT and AMW which are still 13

14 not used for quality controlled ECG compression. In this thesis, above unexplored transforms are used for ECG compression. The results so obtained are compared with transform based techniques reported in the literature. It has been found from the comparison that at low PRD, DCT, CPT and WAT perform better than LPT, SLT and AMW. So for better results, use of these transforms cannot be organized for ECG compression. To further improve the performance the concept of normalization is also introduced in this thesis. To improve the CR for quality controlled ECG compression an additional step is proposed after transformation and before thresholding which is known as normalization of coefficients. So the revised algorithm will have transformation, normalization, thresholding, quantization, and encoding steps. It is shown in the results that the normalization of coefficients before thresholding improves the CR. For example, the record MIT-BIH 121 at UPRD=0.5, CR is for DCT and at UPRD=2, CR is for LWT without normalization and with normalization CR increases to for DCT and it increases to for LWT. For quality controlled ECG compression to calculate the threshold value the researchers mostly used the bisection algorithm (BA). BA method is used in thesis along with Genetic Algorithm (GA) method. It is observed that BA and GA are equally good to calculate the threshold value in general. But in particular, GA performs better for low PRD because the desired threshold value obtained through GA is more closed to the optimized value. BA is preferred for high PRD over GA, because its takes less computational time for calculating the threshold value as compared to GA. Along with linear transforms, non linear transforms are also analyzed and studied. In literature, most of the work is based on WT which has disadvantage that at discontinuities, a large number of coefficients appear resulting in the decline in compression ratio. To 14

15 overcome this disadvantage, ENO interpolation (non linear transform) technique is used in this thesis. This technique avoid discontinuities, therefore lesser number of coefficients appear in the transform domain. This leads to better CR. The same can be verified from the results presented. WT requires large number of computation and large storage that are not desirable for either high speed or low power application. So, lifting schemes are used in this thesis to overcome these problems. In this thesis, lifting schemes are applied for quality controlled ECG compression. From the results, it is analyzed that LWT performance is better than linear and non linear transforms at high PRD. In nutshell, transformation based efficient and tunable ECG compression techniques are proposed in this thesis. Out of the work presented in thesis some paper are produced and published in the Journals and conferences as per the list given at page no. x and xi. 15