A portable respiration evaluation system using on-line segmental empirical mode decomposition

As mHealth (Mobile Health) thrives, advances in collection and analysis of vital signals on portable devices have become more and more important. In this paper, a low end effect on-line segmental empirical mode decomposition (SegEMD) is proposed. SegEMD is capable of processing continuous signals segment by segment with EMD, by reusing the slopes, the previous data and the estimation of signal characteristics in advance. Worst normalized mean squared error (NMSE) compared to the results carried out by the conventional EMD is less than 9%. For decomposing an 8-hour overnight electrocardiogram (ECG) signal, the processing time is twice the conventional EMD, but the memory requirement is reduced to below 1%. Compared with SEMD, the processing time is 83% less and the memory used is 63% less. The proposed detrending and extraction of ECG-derived respiration (EDR) also reach 0.72 of correlation coefficient with the respiratory signal from the thoracic belt.