@article{(Open Science Index):https://publications.waset.org/pdf/10013140,
	  title     = {Compressed Sensing of Fetal Electrocardiogram Signals Based on Joint Block Multi-Orthogonal Least Squares Algorithm},
	  author    = {Xiang Jianhong and  Wang Cong and  Wang Linyu},
	  country	= {},
	  institution	= {},
	  abstract     = {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%.},
	    journal   = {International Journal of Information and Communication Engineering},
	  volume    = {17},
	  number    = {6},
	  year      = {2023},
	  pages     = {387 - 395},
	  ee        = {https://publications.waset.org/pdf/10013140},
	  url   	= {https://publications.waset.org/vol/198},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 198, 2023},
	}