Curvelet Transform Based EEG Signal Compression

Abstract

Biomedical signals need to be digitally stored or transmitted with a large number of samples per second, and with a great number of bits per sample, in order to assure the required fidelity of the waveform for visual inspection. Therefore, the use of signal compression techniques is fundamental for cost reduction and technical feasibility of storage and transmission of biomedical signals. Compressive Sensing is an effective method to make data compressed for EEG signals with high compression ratio and good quality of reconstruction. Experimental results show that the curvelet transform compression method performs much better based on Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE).