Compressed Sensing of Fetal Electrocardiogram Signals Based on Joint Block Multi-Orthogonal Least Squares Algorithm
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Compressed Sensing of Fetal Electrocardiogram Signals Based on Joint Block Multi-Orthogonal Least Squares Algorithm

Authors: Xiang Jianhong, Wang Cong, Wang Linyu


With the rise of medical IoT technologies, Wireless body area networks (WBANs) can collect fetal electrocardiogram (FECG) signals to support telemedicine analysis. The compressed sensing (CS)-based WBANs system can avoid the sampling of a large amount of redundant information and reduce the complexity and computing time of data processing, but the existing algorithms have poor signal compression and reconstruction performance. In this paper, a Joint block multi-orthogonal least squares (JBMOLS) algorithm is proposed. We apply the FECG signal to the Joint block sparse model (JBSM), and a comparative study of sparse transformation and measurement matrices is carried out. A FECG signal compression transmission mode based on Rbio5.5 wavelet, Bernoulli measurement matrix, and JBMOLS algorithm is proposed to improve the compression and reconstruction performance of FECG signal by CS-based WBANs. Experimental results show that the compression ratio (CR) required for accurate reconstruction of this transmission mode is increased by nearly 10%, and the runtime is saved by about 30%.

Keywords: telemedicine, fetal electrocardiogram, compressed sensing, joint sparse reconstruction, block sparse signal

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[1] S. Kumar, B. Deka and S. Datta, Weighted Block Compressed Sensing for Multichannel Fetal ECG Reconstruction, 2019 IEEE Region 10 Conference (TENCON). (2019) 2324-2328.
[2] D. L. Donoho, Compressed sensing, IEEE Transactions on Information Theory. 52(4) (2006) 1289-1306.
[3] Zhang Y C, Zhang W, Xu H Y, Epidemiological Aspects, Prenatal Screening and Diagnosis of Congenital Heart Defects in Beijing Frontiers in Cardiovascular Medicine. (2021).
[4] H. Mamaghanian, N. Khaled, D. Atienza and P. Vandergheynst, Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes, IEEE Transactions on Biomedical Engineering. 58(9) (2011) 2456-2466.
[5] M. Hasan, M. Reaz, M. Ibrahimy, M. Hussain, and J. Uddin, Detection and processing techniques of FECG signal for fetal monitoring, Biological procedures online. 11(1) (2009) 263–295
[6] J. Zhang, Z. L. Yu, Z. Gu, Y. Li and Z. Lin, Multichannel Electrocardiogram Reconstruction in Wireless Body Sensor Networks Through Weighted l1,2 Minimization, IEEE Transactions on Instrumentation and Measurement. 67(9) (2018) 2024-2034.
[7] Lu M, Chen W, Xia S, et al, Random Chirp Frequency-stepped Signal ISAR Imaging Algorithm Based on Joint Block-sparse Model ISAR, Journal of Electronics and Information Technology. 40(11) (2018) 2614-2620.
[8] Kumar S, Deka B, Datta S, Multichannel ECG Compression using Block-Sparsity-based Joint Compressive Sensing, Circuits Systems and Signal Processing. 39(12) (2020) 6299-6315.
[9] Zhang H P, Dong Z R, Wang Z, et al, CSNet: A deep learning approach for ECG compressed sensing, Biomedical Signal Processing and Control. 70 (2021).
[10] Qu Xin-chao, Zhang Yue, Real-time ECG compression algorithm based on compressed sensing, Computer Engineering and Design. 35(10) (2014) 3450-3454,3479.
[11] Mishra A, Thakkar F, Modi C, et al, Comparative analysis of wavelet basis functions for ECG signal compression through compressive sensing, International Journal of Computer Science and Telecommunications. 3(4) (2012) 23-31.
[12] R. Sameni, The open-source electrophysiological toolbox (OSET), 2012. (Online). Available:
[13] Tropp J, Gilbert A C, Signal recovery form random measurements via orthogonal matching pursuit, IEEE Transactions on Information Theory. 53(12) (2007) 4655-4666.
[14] Zhang Y T, Liu C Y, Wei S S, et al, ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix, Journal of Zhejiang University-Science C-Computers & Electronics. 15(7) (2014) 564-573.
[15] R. R. Naidu, P. Jampana and C. S. Sastry, Deterministic Compressed Sensing Matrices: Construction via Euler Squares and Applications, IEEE Transactions on Signal Processing. 64(14) (2016) 3566-3575.
[16] A. Elzanaty, A. Giorgetti and M. Chiani, Limits on Sparse Data Acquisition: RIC Analysis of Finite Gaussian Matrices, IEEE Transactions on Information Theory. 65(3) (2019) 1578-1588.
[17] Chen S, Billings S A, Luo W, Orthogonal least squares methods and their application to non-linear system identification, International Journal of contrl. 50(5) (1989) 1873-1896.
[18] H. F. Schepker and A. Dekorsy, Compressive Sensing Multi-User Detection with Block-Wise Orthogonal Least Squares, 2012 IEEE 75th Vehicular Technology Conference (VTC Spring). (2012) 1-5.
[19] J. Kim and B. Shim, Multiple Orthogonal Least Squares for Joint Sparse Recovery, 2018 IEEE International Symposium on Information Theory (ISIT), (2018) 61-65.
[20] W. Dai and O. Milenkovic, Subspace Pursuit for Compressive Sensing Signal Reconstruction, IEEE Transactions on Information Theory. 55(5) (2009) 2230-2249.
[21] Wang L W, Wang X, Feng J F, Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition, Pattern Recognition. 39(3) (2006) 456-464.
[22] Y. C. Eldar, P. Kuppinger and H. Bolcskei, Block-Sparse Signals: Uncertainty Relations and Efficient Recovery, IEEE Transactions on Signal Processing. 58(6) (2010) 3042-3054.