【作者】 陈颖；

【导师】 王哲龙；

【作者基本信息】 大连理工大学 ， 控制理论与控制工程， 2007， 硕士

【摘要】 随着现代化医疗设备的增加，医学数据量激增。为了减少存储量和有利于数据传输，需要在不损失诊断信息的前提下对原始数据进行压缩。特别是随着计算机在心脏病诊断，监护等领域的广泛应用和心电图数量的日益增长，数据压缩技术自本世纪六十年代开始应用于心电图领域，并不断得到发展。心电(Electrocardiogram，ECG)数据压缩可减少传送ECG数据所需的信道带宽和传送时间，减小用于存贮ECG数据的空间。基于小波变换的压缩算法在图像及信号压缩领域得到了广泛的应用。小波变换用于信号编码的基本思想就是把信号进行多分辨率分解，分解成不同频率的近似系数和细节系数，然后再对分解后的系数进行编码。系数编码是小波变换用于压缩的核心，压缩的实质是对系数的量化压缩。采用何种策略对小波变换后的数据进行处理仍是信号压缩领域的一个研究热点。本论文在SPIHT(Set Partitioning In Hierarchical Trees)编码算法的基础上，通过引入提升小波变换、阈值优化选取、扩充零树结构等措施，提出了一种新的嵌入零树小波ECG信号压缩编码算法——Modified SPIHT，简称为MSPIHT算法。算法首先将ECG数据进行提升小波变换；其次，对提升小波变换后的小波系数进行阈值优化选取，最后用基于扩充零树结构的改进的SPIHT算法对系数进行编码。对本文给出的压缩算法方案进行了仿真实验，实验分成两部分。第一部分实验是对ECG信号进行一维压缩，通过对MIT-BIH心律不齐数据库中记录的信号数据进行的压缩实验，验证了MSPIHT算法的有效性。同时，将MSPIHT算法与SPIHT算法及其它基于小波变换的压缩方法的压缩效果进行了比较，结果表明MSPIHT算法具有更好的压缩效果。第二部分实验是利用MSPIHT算法对ECG信号进行二维压缩，这样在保证信号重建质量的情况下，可以获得比用MSPIHT算法进行一维ECG信号压缩更高的压缩比。两部分实验验证了MSPIHT算法用于ECG信号压缩的有效性。更多还原

【Abstract】 The increasing of modern medical equipment leads to the explosion of the quantity of medical data. In order to decrease the quantity of memory needed, the original data have to be compressed, while the diagnose information can’t be damaged. With the computers’ extensive application in the field of heart diseases diagnosis and patient monitoring, data compression technology began to find its application on ECG (Electrocardiogram) data in 1960s. ECG data compression can decrease the channel bandwidth used to transmit the data and the memory consumed to store the data.Wavelet-based compression scheme has been put into practice in the area of compression of signal. The basic ideology of the wavelet based compression algorithm is the signal multi-resolution decomposition. Then encode the decomposition coefficient. Coefficient encoding is the core of wavelet-based compression algorithm. So, the encoding algorithm is a hotspot in the compression area.In this paper, a new ECG signal compression algorithm named Modified Set Partitioning In Hierarchical Trees (MSPIHT), which is based on the SPIHT(Set Partitioning In Hierarchical Trees) algorithm, has been proposed. Several measures have been taken to improve the SPIHT algorithm and these measures include that the lifting wavelet transform is adopted, the extended zerotree structure is redefined, and the threshold optimization.Simulation experiments by using the MSPIHT algorithm and several other compression algorithms have been conducted to verify the validity of the MSPIHT algorithm and test its performance. The experimental results show that the proposed MSPIHT algorithm is valid and can achieve the better compression performance than other compression algorithms based on the wavelet transform. To achieve bigger compression ratio, we also use the proposed algorithm to compress two dimensional ECG data.更多还原