Cheng Yih-Chun, Tsai Pei-Yun, Huang Ming-Hao
IEEE Trans Biomed Circuits Syst. 2016 Aug;10(4):864-873. doi: 10.1109/TBCAS.2016.2539244. Epub 2016 May 19.
Low-complexity compressed sensing (CS) techniques for monitoring electrocardiogram (ECG) signals in wireless body sensor network (WBSN) are presented. The prior probability of ECG sparsity in the wavelet domain is first exploited. Then, variable orthogonal multi-matching pursuit (vOMMP) algorithm that consists of two phases is proposed. In the first phase, orthogonal matching pursuit (OMP) algorithm is adopted to effectively augment the support set with reliable indices and in the second phase, the orthogonal multi-matching pursuit (OMMP) is employed to rescue the missing indices. The reconstruction performance is thus enhanced with the prior information and the vOMMP algorithm. Furthermore, the computation-intensive pseudo-inverse operation is simplified by the matrix-inversion-free (MIF) technique based on QR decomposition. The vOMMP-MIF CS decoder is then implemented in 90 nm CMOS technology. The QR decomposition is accomplished by two systolic arrays working in parallel. The implementation supports three settings for obtaining 40, 44, and 48 coefficients in the sparse vector. From the measurement result, the power consumption is 11.7 mW at 0.9 V and 12 MHz. Compared to prior chip implementations, our design shows good hardware efficiency and is suitable for low-energy applications.
提出了用于无线体域网（WBSN）中监测心电图（ECG）信号的低复杂度压缩感知（CS）技术。首先利用了小波域中ECG稀疏性的先验概率。然后，提出了由两个阶段组成的可变正交多匹配追踪（vOMMP）算法。在第一阶段，采用正交匹配追踪（OMP）算法以可靠的索引有效地扩充支撑集，在第二阶段，采用正交多匹配追踪（OMMP）来挽救缺失的索引。因此，利用先验信息和vOMMP算法提高了重建性能。此外，基于QR分解的无矩阵求逆（MIF）技术简化了计算密集型的伪逆运算。然后在90 nm CMOS技术中实现了vOMMP-MIF CS解码器。QR分解由两个并行工作的脉动阵列完成。该实现支持三种设置，以在稀疏向量中获得40、44和48个系数。从测量结果来看，在0.9 V和12 MHz时功耗为11.7 mW。与先前的芯片实现相比，我们的设计显示出良好的硬件效率，适用于低能耗应用。
