Near-lossless compression for multichannel EEG using empirical mode decomposition

At present, Covid 19 cases are continually being reported all around the world. There exists an extreme shortage of specialist physicians which are being reported, which in turn affects the treatment of the pandemic disaster. The health sector is forcibly being switched to telemetry diagnoses and treatments. Hence, it becomes necessary to develop an efficient compression system for transmission and storage of applications in short time with great efforts. For biomedical applications, neurologists require an efficient system which provides more accurate and error free data once the signal is reconstructed. The aim is to improve the compression ratio and minimize the reconstruction error of electroencephalographic signal, designed by a two-stage compression scheme. Here, an empirical mode decomposition technique is used to breakdown the signal. The overall compression ratio (CR) of this method is 12.5:1. The transmitted EEG signals and the reconstructed EEG signal are found to be almost same with a percentage rate of distortion of 5.4%. In comparison with other lossless compression techniques, the proposed method offers high compression rate with a minimum probability of error.