The comparison of different transform based methods for ECG data compression

Electrocardiogram (ECG) is widely used in the diagnosis and treatment of cardiac disease. Large amount of signal data needs to be stored and transmitted. So, it is necessary to compress the ECG signal data in an efficient way. In the past decades, many ECG compression methods have been proposed and these methods can be roughly classified into three categories: direct methods, parameter extraction methods and transform methods. In this paper a comparative study of Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) and Wavelet Transform (WT) transformations is carried. Records selected from MIT-BIH arrhythmia database are tested. For performance evaluation Compression Ratio (CR), Percent Root Mean Square difference (PRD) and Signal to Noise Ratio (SNR) parameters are used. Simulation results shows that using FFT low PRD and high SNR is achieved. DCT increases CR by 58.97% than FFT. WT further increases CR by 31% than DCT with low PRD value. It shows that ECG data compression using wavelet transform can achieve better compression performance than FFT and DCT.