Table 3.19: Performance comparison of specificmitarecords. Here, M = 4096 and N = 2048.
mita WPFDEC [121] Proposed TDR
record CR PRD (%) PRD1 (%) CR PRD (%) PRD1 (%) RMS (µV) NCC WMOSe(%)
117 8.36 1.34 4.95 8.39 1.130 4.102 9.8 0.9992 0.0
232 7.35 5.0 8.68 7.37 4.059 8.03 9.3 0.9902 0.0
Table 3.20: Evaluation of clinical information by WMOSerror at target CDR with and without entropy coder(EC) and the maximum execution time,tefor encoding and decoding process.
10 s of each record of dataset-V (CDR = 495 bps andε = 2.5%) 1 s of 12-lead ECG (CDR = 792 bps)
Target WMOSerr(%) maximum WMOSerr Execution time
algorithm 15mitarecords cu01 cu04 800 801 te(s) (%) te(s)
w/ EC 0.0 0.0 0.0 28.5 34.38 1.42 2.75 1.762
w/o EC 0.0 0.0 0.0 36.2 37.6 0.498 4.6 0.682
Quality groups defined by MOSerr[191]: 0≤very good<15,15≤good<35,35≤not good<50,bad≥50
100% was achieved for CR≤8 and CR≤4 for ECG records fromcuvtandmitsvadatabase, respectively.
All of the diagnostic features of the ECG are easily recognized and are faithfully reproduced, and the various beat morphologies are clearly distinguished within the above determined CR/CDR ranges. There is no standard objective measure for defining the clinically acceptable distortion level of the reconstructed signal. Even if the range of error value is defined, the experimental results of the TDL algorithm show that the variation in the CDR may not always fulfill the requirements of the dedicated transmission link.
However, the performance of the presented TDL and TDR driven wavelet threshold based algorithm for the compression of the ECG signals is better than other wavelet based ECG coders.
The data rate variability of TDL algorithm is analyzed under different signal conditions such as mean value variation, noise level and time varying PQRST morphologies. The compression performance of the TDL and TDR based compression algorithms are compared. The issues on the use of PRD/RMSE as a quality measure are analyzed. The PRD distributes the error equally over all portions of the ECG signal and the measure fails to characterize the local distortion of an ECG signal. It is observed that the PRD criterion is not a subjectively meaningful measure since small and large numerical distortions do not correspond to “good” and “bad” subjective quality, respectively. Thus, the range of PRD value defined in reported algorithms did not necessarily result in clinically acceptable signal.
In literature, the tests are carried out using the well-known mitadatabase which contains many time- varying and noise-contaminated ECG signals. Experiments show that the WT based method may produce smooth reconstructed signal. The noise in the input decreases the compression rate of the coder since the coder will spend extra bits on approximating the noise with the specified accuracy. Thus, the distortion measurement criterion plays an important role for choosing a set of optimal coding parameters. Therefore, we focussed on the evaluation of the TDR algorithm rather than the TDL algorithm in this Chapter. In this case, the subjective evaluation is used to quantify the dissatisfaction of the compressed ECG signal.
Although the subjective test is the obvious way of measuring clinical quality, such a test is tedious, time consuming and results depend on various other factors such as the physicians background, motivation, etc. Moreover, it cannot be incorporated into automatic quality controlled compression systems. On the other hand, objective measure is repeatable and simple but it does not always match with the subjective one. However, measurement of quality is crucial because the distortion introduced by different types of compressors are very diverse. The above constructs show that in order to introduce closed loop CR or quality control one needs an adequate distortion measure for the compressed signal. Moreover, the choice of which distortion measure must be used for compressed signal is of critical importance when noise suppression and signal compression are established simultaneously. Therefore, we attempt the diagnostic distortion measure in the next Chapter.
4.1 Introduction
Many wavelet based ECG compression methods are reported and the tests are carried out using the noisy records from themitadatabase and the percentage root mean square difference (PRD) criterion in the liter- ature. A major design goal of any compression method is to obtain the best clinical quality with the highest compression ratio (CR) using the optimal coding parameters such as threshold or/and quantization bit ob- tained for a quality or distortion specification. But the measurement of distortion in the compressed signal is difficult because the distortion introduced by different types of compressors are very diverse. The effect of noise filtering is one of the features using the wavelet transform for compression and it is demonstrated in various compression results reported in the literature. In this case, the magnitude of insignificant errors may not be of much relevance from the point of view of clinical quality of the compressed signal. The ef- fects of noise on the rate-distortion performance of the proposed methods and the SPIHT based methods are demonstrated in the previous chapter. Although PRD does not exactly correspond to the result of a clinical subjective test, it is easy to calculate and compare, so it is widely used in the ECG compression literature.
Thus, in order to introduce closed loop CR or quality control, one needs an adequate diagnostic distortion measure for the compressed signal. Moreover, the choice of which distortion measure must be used for compressed signal is of critical importance when noise suppression and signal compression are established simultaneously. In the area of ECG signal compression, little attention has been paid towards the evaluation of distortion of clinical information. A suitable objective distortion measure can help proper evaluation of the well-designed ECG compression methods under noisy environments. Otherwise, the quality of the compressed signal has to be evaluated by subjective test, visual inspection of the clinical features. However, performing subjective test is a difficult task in closed loop rate or quality control method in which the op- timal coding parameters are adaptively chosen to compress time-varying PQRST morphology effectively.
A number of researchers have proposed variety of speech and image quality measures for local and global assessment. But very little effort has been made to provide an objective quality measure for the assessment of distorted ECG signal. The compression system typically involves tradeoff between the rate and quality of the output. Undoubtedly, there is a need for an objective measure for local and global assessment, and thus assessment of distorted signal quality is an open problem today.
In this chapter, a novel wavelet energy based diagnostic distortion measure is proposed for compressed ECG signal quality assessment. The proposed measure is a weighted percentage root mean square dif- ference between subband coefficients of the original and compressed signals with weights equal to the relative wavelet subband energy of the corresponding subbands. These weights may represent the actual contribution of each subband that are used to discriminate different frequency subbands particularly bands corresponding to noise. The proposed measure appears to be a correct representation of the amount of signal distortion at all scales. Experiments show that the proposed measure works substantially better than the conventional PRD and the wavelet based weighted PRD (WWPRD) measures. The proposed measure
correlates well with subjective assessments and leads to provide a better evaluation of rate-distortion per- formance of the compression method. This Chapter is organized as follows. Section 4.2 discusses ECG distortion measures and their limitations. In Section 4.3, a novel wavelet energy based diagnostic distortion measure is proposed. In Section 4.4 preliminary evaluation of the WEDD measure is presented. In Section 4.5, subjective quality assessment of the clinical features of the compressed signal is presented. In Section 4.6, quantitative and qualitative analysis of the WEDD measure is performed.