Compression of ECG Signal Based on Improving the Signal Sparsity Adopting QRS-Complex Estimation and Spatial Domain Transforms | ||
| Menoufia Journal of Electronic Engineering Research | ||
| Article 4, Volume 25, Issue 2, July 2016, Pages 179-190 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/mjeer.2016.64095 | ||
| Authors | ||
| Mohammed Abo-Zahhad1; Aziza Hussein2; Abdelfatah Mohamed1 | ||
| 1Electrical and Electronics Eng. Dept., Faculty of Eng., Assiut University, Egypt. | ||
| 2Computer and Systems Dept., Faculty of Engineering, Minia University, Egypt. | ||
| Abstract | ||
| In this paper, an ECG signal compression technique based on improving the signal sparsity adopting QRS-complex estimation and spatial domain transforms is proposed. Compressive Sensing (CS) framework is utilized for this purpose; where the difference between the rate of change of a signal and the rate of change of its information contents is utilized. The proposed method starts with estimating the QRS-complex through the detection of the maximum amplitudes and the start and end points of the signal components. The error signal calculated as the difference between the estimated QRS-complex samples and original ECG samples is transformed using either DWT, DCT or FFT. QRS-complex estimation results in sparser timedomain error signal and the sparsity is increased by adopting the spatial-domain transforms. The proposed technique is assessed by calculating PRD and CR. Numerical results indicate that DWT gives a higher CR and lower PRD. The effect of increasing the signal sparsity, signal length, wavelet filters and wavelet decomposition levels have been studied. Comparison with recently published results [1] adopted only QRS-estimation and DWT indicate that the utilization of CS with both QRS-estimation and DWT yields to improved acceptable retrieved signal quality and higher CR. Moreover, comparison with other four CS-based algorithms [2]-[5] and other traditional ECG compression algorithm [6] indicates the superior performance of the proposed algorithm. . | ||
| References | ||
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