Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a low-power wireless body-area network for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing data with low power consumption. However, due to some specific characteristics of FECG recordings such as non-sparsity and strong noise contamination, current CS algorithms generally fail in this application. In this work we utilize the block sparse Bayesian learning (bSBL) framework, a recently developed framework solving the CS problems. To illustrate the ability of the bSBL methods, we apply it to two representative FECG datasets. In one dataset the fetal heartbeat signals are visible, while in the other dataset are barely visible. The experiment results show that the bSBL framework is capable of compressing FECG raw recordings and successfully reconstructing them. These successes rely on two unique features of the bSBL framework; one is the ability to reconstruct less-sparse but structured signals, and the other is the ability to learn and exploit correlation structure of signals to improve performance. These two abilities of the framework greatly enhance the potential use of bSBL in telemonitoring of other physiological signals.

使用块稀疏贝叶斯学习框架对非稀疏原始FECG记录进行压缩/重建，实验结果表明该框架可以高质量重建记录，并可以使用稀疏二进制感测矩阵压缩记录。

通过块稀疏贝叶斯学习进行压缩感知，实现无创胎儿ECG的能效无线遥测