ECG data compression by matching pursuits with multiscale atoms

In this paper. we propose an ECG waveform compression technique based on th e matching pursuit. The matching pursuit is an iterative non-orthogonal sig nal expansion technique. A signal is decomposed tt atoms in a function dict ionary. The constraint to the dictionary is only the over-completeness to s ignals. The function dictionary can be defined to be best match to the stru cture of the ECG waveform, In this paper, we introduce the multiscale analy sis to the implementation of inner product computations between signals and atoms in the matching pursuit iteration. The computational cost Call be re duced by utilization of the filter bank of the multiscale analysis. We show the waveform approximation capability of the matching pursuit with multisc ale analysis. We show that a simple 4-tap integer filter bank is enough to the approximation and compression of ECG waveforms. In ECG waveform compres sion. we apply the error fc-l-back procedure to the matching pursuit iterat ion to reduce the norm of the approximation error. Finally. actual ECG wave form compression by the proposed method are demonstrated. The proposed meth od achieve the compression by the factor 10 to 30. The compression ratio gi ven by the proposed method is higher than the orthogonal wavelet transform coding in the range of the reconstruction precision lower than 9% in PRD.