5.5 Results and Discussions
5.5.1 Evaluation of the reconstruction results
The main objective of this subsection is to demonstrate the effectiveness of the weighting strategy proposed in this work for better reconstruction at the low measurement rates (higher compression ratios). Visual evaluation test [45] is done to verify the preservation of important diagnostic ECG features in the reconstructed MECG signals. It involves visual analysis of different pathological ECG features in the reconstructed waveforms. To make visualization more clear, the original and the re- constructed ECG signals corresponding to the first 4096 time points (approx. 4-sec data) of the PTB database from different channels are plotted together. Figure 5.3 depicts a bundle branch block (BBB) pathology reconstruction (data-record s0424) and Figure 5.4 shows a cardiomyopathy data recovery (data-record s0423) using the proposed SWMNM and PWMNM techniques, respectively, at a low number of measurements, M = 70. In both of the above recovery, the reconstruction error is also plotted along with the reconstructed signals. By observing the reconstruction error plot in both the figures, it is clear that both the proposed techniques significantly reduce the reconstruction error com- pared to the regular (non-weighted) MNM at the same value ofM. The SWMNM technique preserves the important diagnostic features of BBB pathology (encircled in the original signal waveforms) such
5.5 Results and Discussions
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Figure 5.4: Original and recovered signals (first 4096 samples, around 4-sec data) (a) using regular non- weighted MNM technique, (b) using proposed PWMNM technique when the number of measurements,M = 70.
Data-set s0423 rem exhibiting cardiomyopathy is used from the PTB database. Characteristic pathological features are encircled in the original signal waveforms. Arrows in the reconstruction plots indicate the distortion points.
5. Exploiting Multi-scale Signal Information in Joint Compressed Sensing Recovery
asQRScomplex duration, secondaryR-wave (RSr′ complex) and slurredS-wave (Figure 5.3(b)). On the other hand, severe distortions can be noted in the above diagnostic features in the reconstructed MECG signals using regular MNM (Figure 5.3(a)). Similar observations are also made in Figure 5.4, where the MECG signals belong to cardiomyopathy arrhythmia. The regular MNM fails to recover the clinically important features like pathological Q-wave, R-wave amplitude, and T-wave (Figure 5.4(a)), while PWMNM technique preserves the same (Figure 5.4(b)). The proposed WMNM tech- niques exploit the joint correlation of MECG wavelet coefficients to incorporate multi-source priors about wavelet subbands having high clinical relevance in the joint optimization problem formulation.
This helps WMNM to emphasize the diagnostically important wavelet coefficients and improve the joint MECG recovery.
Signal reconstruction is not the ultimate goal in WBAN-enabled telemonitoring applications. The acquired and transmitted signals through WBAN have to be examined by the cardiologist at healthcare centers for disease detection and classification purposes [1, 41]. Also, the popular distortion measure P RDis not a diagnostic distortion measure and hence a low P RDmay not necessarily correspond to clinically good reconstruction [1]. So, clinical evaluation or diagnosability of the reconstructed ECG signals from the disease classification point of view also becomes important [36]. We followed the steps suggested in [145] for extracting the multi-scale wavelet energies and eigenvalues of multi-scale covari- ance matrices, which are further used as diagnostic features for classification purpose. Support vector machine (SVM) with RBF kernel is used as the classifier. Classification results for MI and HC classes are given in Table 5.1. Classification is performed at different number of measurements, M. Ground truth accuracy of the classifier was 91.5%. It can be seen that the proposed WMNM delivers better classification results compared to its non-weighted counterpart. WMNM gives 73.2% accuracy even when data packet of length N = 512 was recovered using only around 10% measurements (M = 50).
This verifies the good clinical quality even at higher data reduction. On the other hand, MNM per- forms inferior at this value ofM with 62.4% accuracy. It can be noted that the classification accuracy increases when more number of measurements are used for reconstruction. The comparison between the performance of the proposed method and wavelet-based SPIHT method is also carried out. The SPIHT method is implemented using open-source SPIHT software [146, 147]. Evaluation results in the form of classification accuracy for the PTB database at three different CR values (or M values)
5.5 Results and Discussions
Table 5.1: Classification accuracy (diagnosibility) of the system when joint MECG recovery is done by different methods at different number of measurements. Datasets are taken from the PTB database.
Class M Ground Truth Classification accuracy (%)
(%) MNM PWMNM SPIHT
91.5 (Original signal
is used) HC/MI
50 62.4 73.2 65
60 66.4 75.2
70 67.2 77.2 70.3
80 70.8 78.0
100 73.6 80.4 75.6
are given in Table 5.1. It is noted that, despite having better performance in case of single channel ECG compression, as established in earlier studies [1, 45], SPIHT algorithm does not yield encourag- ing performance in case of MECG signal compression. This is clear by observing the classification accuracy of SPIHT, which is also an indirect assessment of reconstruction accuracy (Table 5.1). The SPIHT method performs marginally better than the traditional MNM technique, but underperforms the proposed method. It ignores the spatial correlation as it is designed to process each channel in- dividually, whereas the proposed method exploits it during simultaneous compression/reconstruction.
This might be the reason behind the lower performance by SPIHT in the case of MECG signals.
Average distortion values in terms of average P RD for HC and four major pathological classes present in the PTB database are given in Table 5.2 at M = 100. Class specific average results are calculated over 52 healthy control, 148 myocardial infarction, 7 hypertrophy, 15 bundle branch block, and 18 cardiomyopathy datasets available in the PTB database in order to analyze the inter- class performance variation of the proposed method. The output P RD value in each class falls in good category reconstruction. It is also evident from the table that each class is having variable reconstruction performances at the same number of measurements in each channel. Each class is having its own diagnostic ECG features, which results in different morphological changes in the signals from different channels. This may change the joint sparse representation of MECG resulting in different recovery performance of the proposed algorithm for each class. The average P RD calculated across different classes also ensures the good quality reconstruction. To highlight the variability among the set of ECG data-records, average output P RD of all the pathological data records of PTB database
5. Exploiting Multi-scale Signal Information in Joint Compressed Sensing Recovery
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Figure 5.5: Box plot showing variation of outputP RDacross the data records of PTB database when recovered using PWMNM at different number of measurements.
for Lead aV R is shown in the form of box plots in Figure 5.5. The extreme values in the individual box plot depict the minimum and maximum P RD values obtained at a particular value of M using different datasets. With more compression of the data by reduction in the size of sensing matrix (with lowM values), outputP RD increases and vice-versa.
5.5ResultsandDiscussio Table 5.2: Channel-wise reconstruction error in terms of average outputP RDvalue for different types of normal and pathological cases using the proposed technique when the number of measurements,M = 100. Results are averaged over all the data-sets available in that particular class in the PTB database.
Pathological classes No. of Datasets Different ECG channels
Lead I Lead II Lead III aVR aVL aVF V1 V2 V3 V4 V5 V6 Healthy control 52 8.35 3.75 5.87 5.03 4.30 3.47 4.00 4.21 4.36 5.44 5.09 5.18 Myocardial Infarction 148 10.17 6.96 7.78 5.39 10.96 6.57 5.64 4.60 4.11 4.84 6.77 7.07 Hypertrophy 7 6.42 4.30 4.70 4.77 5.23 4.77 2.77 2.56 3.18 3.74 3.90 3.46 Bundle branch block 15 4.19 4.26 5.53 4.41 3.62 3.52 7.05 4.77 4.33 5.18 4.36 4.27 Cardiomyopathy 18 5.71 4.11 3.69 3.51 4.19 3.11 2.98 3.03 3.23 3.53 2.88 2.60 Average 240 6.96 4.67 5.51 4.62 5.66 4.28 4.48 3.83 3.84 4.54 4.60 4.51
123
5. Exploiting Multi-scale Signal Information in Joint Compressed Sensing Recovery
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Average Joint PRD
SWMNM PWMNM MNM BP SOMP IR−l1/l
2
VG G
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Figure 5.6: Comparison plot between proposed techniques (SWMNM, PWMNM) and existing techniques at different compression ratios (CR) in terms of average jointP RD. Results are averaged over all data-sets taken from the PTB database.