Barlas G D, Skordalakis E S
NCSR DEMOCRITOS Institute of Informatics and Telecommunications, Athens, Greece.
IEEE Trans Biomed Eng. 1996 Aug;43(8):820-8. doi: 10.1109/10.508544.
In this paper, a novel family of compression algorithms is presented, which is designed to exploit the redundancy of one-dimensional (1-D) semiperiodical biomedical signals resulting from the cyclic nature of the underlying physical process. The basic idea is that a pool of past-seen cycles is maintained and cycles to be encoded can be stored as transformed versions of those residing in the pool. Conceptually, this approach is an extension of dictionary-based coding schemes used for text compression to signal patterns residing in an n-dimensional space. A cycle transformation method is introduced in order to render the pattern matching process practical and to enable cycle substitution. Based on the principles of the algorithmic family and this transformation method, an electrocardiogram (ECG)-oriented algorithm is implemented and thoroughly tested. The performance of this implementation is examined theoretically and deductions about the optimal algorithm settings are made. The ECG compression algorithm is superior to the average beat subtraction algorithm as proposed by Hamilton and Tompkins in cases where high compression ratios are required.
本文提出了一种新型的压缩算法族，其设计目的是利用一维（1-D）半周期性生物医学信号由于潜在物理过程的循环性质而产生的冗余。基本思想是维护一个过去见过的周期池，并且要编码的周期可以作为池中周期的变换版本进行存储。从概念上讲，这种方法是用于文本压缩的基于字典的编码方案到n维空间中信号模式的扩展。引入了一种周期变换方法，以使模式匹配过程切实可行并实现周期替换。基于该算法族的原理和这种变换方法，实现了一种面向心电图（ECG）的算法并进行了全面测试。从理论上检查了该实现的性能，并对最优算法设置进行了推导。在需要高压缩率的情况下，该ECG压缩算法优于Hamilton和Tompkins提出的平均心跳减法算法。
