Performance Analysis of ECG Signal Compression using SPIHT

In this paper, we analyze the performance of electrocardiogram (ECG) signal compression by comparing original and reconstructed signal on two problems. First, automatic sleep stage classification based on ECG signal; second, arrhythmia classification. An effective ECG signal compression method based on two-dimensional wavelet transform which employs set partitioning in hierarchical trees (SPIHT) and beat reordering technique used to compress the ECG signal. This method utilizes the redundancy between adjacent samples and adjacent beats. Beat reordering rearranges beat order in 2D (2 dimension) ECG array based on the similarity between adjacent beats. The experimental results show that the proposed method yields relatively low distortion at high compression rate. The experimental results also show that the accuracy of sleep stage classification and arrhythmia classification using reconstructed ECG signal from proposed method is comparable to the original signal. The proposed method preserved signal characteristics for the automatic sleep stage and arrhythmia classification problems.