Dynamic spectrum extraction method based on independent component analysis combined dual-tree complex wavelet transform
Dynamic spectrum (DS) has major significance in the non-invasive measurement of blood components. Effective dynamic spectrum extraction methods can enhance the signal to noise ratio (SNR) of dynamic spectrum data and improve the non-invasive measurement accuracy. According to the principle of DS and the characteristics of the photoplethysmogram (PPG), in this paper, a new dynamic spectrum extraction method based on independent component analysis (ICA) combined with dual-tree complex wavelet transform (DTCWT) is proposed. The core of this method is to find out the closest ratio between each PPG signal at one wavelength and the template signal of PPGs which represents original blood pulsating information best as DS data. In order to testify the effectiveness of the new proposed method, experiments for the determination of hemoglobin concentration of 151 volunteers based on three different kinds of DS extraction methods coupled with partial least squares (PLS) were conducted respectively, where the root mean square error of the calibration (RMSEC), root mean square error of prediction (RMSEP), correlation coefficient of calibration (Rc) and correlation coefficient of prediction (Rp) were used as the evaluation index of the prediction performance. Compared with the other two famous DS extraction methods, frequency domain analysis and single trial estimation, ICA combined DTCWT showed better prediction ability. The forecast accuracy of the new method ICA combined DTCWT reached 90.62%, while commonly used frequency domain analysis and single trial estimation was 63.71% and 78.83%, respectively. The results show comprehensively that the dynamic spectrum extraction method based on ICA combined DTCWT is more reliable and accurate, which opens up avenues for the non-invasive study of the dynamic spectrum.