一种基于随机解调结构的心电信号压缩采样方法

A Novel Compressed Sampling Method of ECG Signal Based on Random Demodulator

针对可穿戴装备实时监测心电信号中的大数据量、高功耗问题,提出了一种随机解调结构压缩采样心电信号方法。从压缩率和重构精度角度,分析了随机解调结构的采样相位、滤波器模块参数、单次采样时间及稀疏度阈值对心电信号重构的影响,完成了基于正交匹配追踪(OMP)、广义正交匹配追踪(GOMP)、稀疏度自适应匹配追踪(SAMP)、分段正交匹配追踪(StOMP)、正则化正交匹配追踪(ROMP)、压缩采样匹配追踪(CoSaMP)、子空间追踪(SP)等不同重构算法的最优结果分析,实现了心电信号的亚奈奎斯特频率采样。基于MIT-BIH数据库的仿真实验结果表明:提出的随机解调结构压缩采样方法,可以以亚奈奎斯特频率采样心电信号,通过重构算法达到奈奎斯特频率采样的效果,在可穿戴健康监护装备领域具有显著的低功耗低数据量应用优势。

Aiming at the problem of large amount of sampled data and high power consumption in real-time ECG signal monitoring of wearable devices,a compressed sampling method of ECG signals based on random demodulator is proposed ECG signals based on random demodulator is proposed.Considering the compression ratio and the ECG signal reconstruction accuracy,the effects of sampling phase,filter parameters,single sampling time of random demodulator and sparsity parameters on ECG signal reconstruction are studied,and the optimal results based on different reconstruction algorithms such as OMP,GOMP,SAMP,StOMP,ROMP,CoSaMP and SP are analyzed.Finally,the compressed sampling of ECG signal with sub-Nyquist is realized.The simulation based on MIT-BIH database show that the proposed compressed sampling method with random demodulator can collect the ECG signal at sub-Nyquist frequency,and obtain the similar sampling effect at Nyquist sampling frequency algorithm.It has significant advantages in use for wearable health monitoring devices.