ECG compression and recognition using complete tree representation

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.