Kumaravel N, Koushik R, Nithiyanandam N
School of Electronics and Communication Engineering, Anna University, Chennai, India.
Biomed Sci Instrum. 1997;34:113-8.
This paper deals with a novel method of Electrocardiogram (ECG) representation, compression and recognition using Complete Tree algorithm. The construction of Complete Tree is like overlaying a ECG waveform on a grid. The waveform is observed through the grid structure with vertical and horizontal grid lines. In addition to nodes created for each interval enclosed by that waveform on a quantisation level, nodes are also created for each point where the waveform cuts the vertical grid line between the current quantisation level and the next quantisation level. A leaf node represents the data sample of ECG at that position. For reconstruction of this ECG waveform, only the leaf nodes are used resulting in ECG compression. This representation is also useful in ECG pattern matching and recognition without reconstructing the original waveform. The ECGs with sampling rate of 500 sps are used. A compression ratio (CR) of 4.8:1 with percent RMS difference (PRD) of 8.45% is obtained. The reconstruction of original ECG waveform from its tree representation shows high fidelity in all its complexes of ECG. The results are compared with all other compression techniques such as AZTEC, TP, FAN, DPCM etc. By using Tree merging and splitting algorithms, the matching and recognition of ECG is implemented.
本文探讨了一种使用完全树算法进行心电图（ECG）表示、压缩和识别的新方法。完全树的构建就如同将一个ECG波形叠加在一个网格上。通过带有垂直和水平网格线的网格结构来观察该波形。除了为该波形在量化级别所包围的每个区间创建节点外，还为波形在当前量化级别和下一个量化级别之间与垂直网格线相交的每个点创建节点。叶节点表示该位置处ECG的数据样本。对于该ECG波形的重建，仅使用叶节点，从而实现ECG压缩。这种表示方式在不重建原始波形的情况下，对于ECG模式匹配和识别也很有用。使用采样率为500 sps的ECG。获得了4.8:1的压缩比（CR）和8.45%的均方根误差百分比（PRD）。从其树表示重建原始ECG波形在ECG的所有复合波中显示出高保真度。将结果与所有其他压缩技术（如AZTEC、TP、FAN、DPCM等）进行比较。通过使用树合并和分裂算法，实现了ECG的匹配和识别。
