自动化学报Issue(7)：1421-1432,12.DOI:10.3724/SP.J.1004.2014.01421
基于过完备字典的体域网压缩感知心电重构
ECG Reconstruction of Body Sensor Network Using Compressed Sensing Based on Overcomplete Dictionary
摘要
Abstract
Regarding to tackle the problem of demanding high accuracy reconstruction electrocardiogram (ECG) signal in a remote monitoring center of the body sensor network (BSN) and the low power problem of the body sensor network, this paper proposes a method of ECG reconstruction of body sensor network using compressed sensing based on overcomplete dictionary. The proposed method uses the compressed sensing theory and random binary matrices as the sensing matrix to measure the ECG signal on the sensor nodes. After the measured value is transmitted to the remote monitoring center, the overcomplete dictionary based on K-SVD algorithm training and the block sparse Bayesian learning reconstruction algorithm are used to reconstruct the ECG signal. Simulation results show that the SNR of the compressed sensing reconstruction ECG based on K-SVD overcomplete dictionary method is 5∼22 dB higher than that of the method using discrete cosine transform when the ECG signal compression rate is at 70%∼95%.The method has the advantages of high accuracy of signal reconstruction, low power, and easy hardware implementation.关键词
过完备字典/体域网/压缩感知/心电信号/K-SVDKey words
Overcomplete dictionary/body sensor network (BSN)/compressed sensing/electrocardiogram (ECG)/K-SVD引用本文复制引用
彭向东,张华,刘继忠..基于过完备字典的体域网压缩感知心电重构[J].自动化学报,2014,(7):1421-1432,12.基金项目
国家自然科学基金(61273282),江西省高等学校科技落地计划项目(KJLD13002),江西省科技计划项目(2011BB50030)资助Supported by National Natural Science Foundation of China (61273282), College Science and Technology Ground Plan Project of Jiangxi Province (KJLD13002), and Science and Tech-nology Plan Projects of Jiangxi Province (2011BB50030) （61273282）
