AI Article Synopsis
- The study aimed to enhance the signal-to-noise ratio (SNR) of near-infrared spectra to improve the accuracy of calibration models for noninvasive hemoglobin monitoring.
- Empirical mode decomposition (EMD) was applied to 81 fingertip absorption curves, ultimately analyzing 78 samples after outlier removal.
- The results showed a significant improvement in prediction accuracy, with the correlation coefficient rising from 0.74 to 0.87 and the root mean square error of prediction (RMSEP) decreasing from 12.85 to 8.08 g/L, confirming EMD's effectiveness in this application.
Article Abstract
To increase the signal-to-noise ratio (SNR) of human near infrared (NIR) spectra, so as to improve the stability and precision of calibration model, the empirical mode decomposition (EMD) method was applied. Eighty-one fingertip absorption curves were collected, with the corresponding clinical examination results obtained immediately. By means of outliers detection and removal, finally 78 samples were determined as the research objects. A three-layer back-propagation artificial neutron network (BP-ANN) model was established and worked for prediction. The results turned out that, through EMD method, the prediction correlation coefficient increased greatly from 0.74 to 0.87. RMSEP was reduced from 12.85 to 8.08 g x L(-1). Other indexes were also obviously improved. The overall results sufficiently demonstrate that it is feasible to use EMD method forhigh SNR pulse wave signals, thus improving the performance of noninvasive hemoglobin calibration models. The application of EMD method can help promote the development of noninvasive hemoglobin monitoring technology.
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