摘要在医疗水平不断发展的今天，对医学成像的图像质量也提出了更高的要求。心电图（Electrocardiogram, ECG）信号常用来诊断心脏的生理状态和病理状态，其细节十分 重要，往往数据量很大。为能够实现以用户为中心的远程医疗或是实时监测，本文使用 压缩感知原理，利用 ECG 信号在小波域的稀疏性和观测矩阵的不相干性对图像进行采样， 仅传输数据量相对小的压缩样本即可在数据接收端以高准确率进行图像重构。本文首先 研究单通道 ECG 信号的压缩感知，采用高斯随机矩阵对初始信号进行压缩感知采样，在 重构端利用信号在小波域的稀疏性建立新的压缩感知数学模型，利用正交匹配追踪算法74534
（Orthogonal Matching Pursuit,OMP）对压缩数据进行重构得到小波域的稀疏信号，再 进行小波逆变换得到恢复的 ECG 信号。然后推广到多通道 ECG 信号，建立多测量向量
（Multiple Measurement Vector，MMV）模型，先对各信号分别进行压缩感知，再利用 多通道信号的联合稀疏性进行重构。数据源采用自国际公认标准心电信号数据库之一的 麻省理工心律失常数据库（MIT-BIH Arrhythmia Database）。
毕业论文关键词 心电信号 压缩感知 小波变换 正交匹配追踪 多测量向量
毕 业 设 计 说 明 书 外 文 摘 要
Title Multi-channel ECG Signals Reconstruction Based on Compressed Sensing
Abstract Nowadays, the rapid and continuously development of medical technologies requires higher quality of medical imaging 。Specialists and doctors diagnose the state of the physiological and pathological state of the heart to the electrocardiogram(ECG) signals。 Thus, it’s important to get the details of the ECG signals, which generally requires large storage space。 To realize user-centric telemedicine and real-time monitoring, we utilize the sparsity of ECG signals in wavelet domain and incoherence between measurement matrix and signals to get the samples of ECG signals, which is based on the principle of compressed sensing。 Then we can reconstruct signals at high accuracy by using much less data。 In this paper, we use Gaussian random matrix as the measurement matrix to compress the multi-channel ECG signals in time domain ,then reconstruct them in wavelet domain using the orthogonal matching pursuit (OMP),finally the reconstructed signal is obtained by taking inverse wavelet transform。 Our work uses the standard test test data from MIT-BIH Arrhythmia Database。
Keywords ECG; compressed sensing; wavelet transform; OMP; multiple measurement vectors
本科毕业设计说明书 第 I 页
目 次
1 引言 1
1。1 背景和意义 1
1。3 论文内容安排 2
2 压缩感知原理 4
2。1 信号的稀疏表示 4
2。2 测量矩阵的选取 5
2。3 信号的重构 6
3 多通道ECG信号的压缩感知与重构:http://www.youerw.com/tongxin/lunwen_85169.html