The effect of electrocardiogram signal compression using beat reordering and SPIHT on automatic sleep stage classification

In this paper, we investigate the effect of electrocardiogram (ECG) compression to the automatic sleep stage classification based on ECG signal. 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 from MIT-BIH polysomnographic database. 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 using reconstructed ECG signal from proposed method is comparable to the original signal.