The sparse decomposition and compression of ECG and EEG based on matching pursuits

Based on Gabor over-complete dictionary, combined with genetic algorithm and matching pursuit (MP) algorithm, a fast sparse decomposition on ECG EEG signals has been accomplished and the computational complexity has been successfully reduced. Then, the sequences encoding method based on over-complete dictionary has been presented and a high compression ratio of ECG and EEG signals has been achieved. The results show that after sparse decomposition and atomic dictionary sequence coding, ECG and EEG signals have a compression ratio of 18:1. And the reconstruction error on ECG and EEG signals are 1.06% and 2.15% respectively. Compared with the traditional time-frequency parameter encoding method, this method has higher data compression rate (RA) and less reconstruction error. Meantime, when the ECG and EEG signals are reconstructed with good performance, the denoising effects are also presented. Thus, it provides a solution to the storage and transmission problems for ECG and EEG data.