FECG compressed sensing mode based on joint block sparsity

Abstract

The development of medical Internet of Things technology has enabled the rise of telemedicine. For medical signals, real-time transmission, accuracy of signals and portability of equipment are required to be strict. The telemedicine system based on Compressed Sensing (CS) technology can avoid sampling a large number of redundant information, and reduce the complexity of data processing and computing time. However, the performance of existing Compressed Sensing algorithms in processing and reconstructing medical signals is poor. It is difficult to meet the strict requirements. In this paper, the sparse representation method which is most suitable for Fetal Electrocardiogram (FECG) signals is obtained by comparison experiment. And a Logistics-Tent Sine Bernoulli Measurement Matrix (LTSBM) construction algorithm is proposed for more convenient hardware applications. Based on the characteristics of FECG signals, a Joint Block Multiple Orthogonal Least Squares (JBMOLS) algorithm is proposed to improve the reconstruction performance in this paper. Combined with the above algorithms, the FECG compressed sensing mode is constituted, which is used to improve the compression, sampling and reconstruction performance of FECG signals. The experimental results show that, compared with traditional compressed sensing technology, the FECG compressed sensing mode improves the overall signal reconstruction performance, the running time is greatly reduced, and the practicability is stronger.