Development and Validation of a Photoplethysmography System for Noninvasive Monitoring of Hemoglobin Concentration

Abstract

Background. Traditional invasive hemoglobin (Hb) detection led to delayed diagnosis, operational inefficiency, incorrect critical decision making, and uncomfortable patient experience. To facilitate real-time total hemoglobin (tHb) monitoring, a portable prototype of a noninvasive Hb detection system was developed, and the accuracy of Hb predicted based on partial least squares (PLS) as well as backpropagation artificial neural network (BP-ANN) models was validated. Results. The prototype was combined with a signal processing circuit and a spectrophotometric probe containing 8 wavelength LEDs light source and photodiode array. Laboratory invasive Hb (Lab_tHb) and spot check Hb measurements with PLS (SpHb_PLS) and BP (SpHb_BP) methods were obtained simultaneously by hematology analyzer and the designed system. The invasive and noninvasive estimates of the Hb levels were analyzed using Spearman correlation as well as Bland–Altman plot and receiver operating characteristic (ROC) curve analysis. A total of 238 volunteers had attempted laboratory invasive and noninvasive spot check Hb measurements. Mean Lab_tHb, SpHb_PLS, and SpHb_BP were 13.6 ± 1.80 g/dL, 13.5 ± 1.07 g/dL, and 13.6 ± 1.06 g/dL, respectively. Noninvasive SpHb_PLS (r = 0.61, p<0.001) and SpHb_BP (r = 0.62, p<0.001) had a strong correlation with invasive tHb values. The Bland–Altman plot showed excellent consistency between the proposed noninvasive methods and laboratory invasive reference. In ROC analysis, PLS and BP models were good at predicting Hb ≥ 12 g/dL with area under the curve of 0.828 and 0.824, respectively. Observed differences between invasive and noninvasive Hb measurements displayed no significant correlation with perfusion index values. Conclusions. The result confirmed that noninvasive Hb monitoring had an excellent correlation with traditional invasive Hb measurement. Furthermore, it is suggested that the developed prototype has the potential for the noninvasive detection of Hb concentration with the methods of PLS and BP-ANN.

Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation

“…A non-invasive HGB concentration level prediction approach based on photoplethysmography (PPG) signals was presented. The system analyses PPG characteristics features utilizing a variety of machine learning methods [20]. Following the analysis of the datasets (PPG signals) obtained from 33 individuals who illuminated light with their fingers over the course of ten periods, 40 distinctive characteristics were identified.…”

Ensemble Extreme Learning Machine Method for Hemoglobin Estimation Based on PhotoPlethysmoGraphic Signals

“…Kwon et al [ 12 ] developed a device for collecting PPG signals and utilized ensemble random forest (RF) and extreme gradient boosting to predict Hb concentration. In our previous study [ 13 ], we developed a portable non-invasive Hb detection system based on eight channel PPG signals. Partial least squares (PLS) and backpropagation artificial neural network (BP-ANN) algorithms were used to construct the Hb prediction model, and the result showed that the system has the potential for non-invasive Hb detection.…”