压缩传感理论在心电图信号恢复问题上的研究

ECG Signal Recovery Problem Based on Compressed Sensing Theory

身体传感器网络(body sensor network,BSN)是实时监护病人生理特征的一种新型网络,压缩传感(compressed sensing,CS)是一种全新的信号采集、编解码理论.面对CS理论用于基于BSN的心电信号的恢复问题,针对现有多种小波基作为稀疏基使用时的表现,通过大量实验,希望寻找到一个最优的小波基,使得心电信号能够精确且稳定地恢复.实验结果证明存在一组小波基,能够使得多种心电信号的恢复结果不但精度高,而且鲁棒性强,该研究结果可以为进一步研究心电信号恢复问题提供参考意见.

Body sensor network （BSN） is a new network which can monitor the patient＇s physiological characteristics in real time, thus the network is widely utilized to improve the level of healthcare for people through the computer techniques, such as intelligent information processing, new network services and so on. Compressed sensing （CS） is a new theory of signal acquisition and decoding, which not only breaks through the bottleneck of the law of traditional sampling, but also makes the signal sampling and compression done at the same time. So, CS is extensively used in medicine, astronomy, pattern recognition and so on. For now, CS is paid more and more attention to by many researchers. This paper faces the recovery problem of electrocardiogram （ECG） signals through compressed sensing theory. According to the various performance of all kinds of wavelet basis conducted as sparse basis, we hope to find an optimal wavelet basis that makes the ECG signal restored precisely and stably through a lot of experiments. The experimental results demonstrate that there exists a set of wavelet basis which can make a variety of ECG signals recuperated with high accuracy and strong robustness. The result can provide ideas for discussing the recovery problem of ECG signals furthermore.