Non-invasive prediction of hemoglobin levels by principal component and back propagation artificial neural network

Abstract

To facilitate non-invasive diagnosis of anemia, specific equipment was developed, and non-invasive hemoglobin (HB) detection method based on back propagation artificial neural network (BP-ANN) was studied. In this paper, we combined a broadband light source composed of 9 LEDs with grating spectrograph and Si photodiode array, and then developed a high-performance spectrophotometric system. By using this equipment, fingertip spectra of 109 volunteers were measured. In order to deduct the interference of redundant data, principal component analysis (PCA) was applied to reduce the dimensionality of collected spectra. Then the principal components of the spectra were taken as input of BP-ANN model. On this basis we obtained the optimal network structure, in which node numbers of input layer, hidden layer, and output layer was 9, 11, and 1. Calibration and correction sample sets were used for analyzing the accuracy of non-invasive hemoglobin measurement, and prediction sample set was used for testing the adaptability of the model. The correlation coefficient of network model established by this method is 0.94, standard error of calibration, correction, and prediction are 11.29g/L, 11.47g/L, and 11.01g/L respectively. The result proves that there exist good correlations between spectra of three sample sets and actual hemoglobin level, and the model has a good robustness. It is indicated that the developed spectrophotometric system has potential for the non-invasive detection of HB levels with the method of BP-ANN combined with PCA.

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

“…Furthermore, whether it is a commercial product or a prototype of laboratory research, the wavelengths of LED light sources for Hb monitoring devices are not exactly the same and have their own characteristics. According to the previous studies, the number of wavelengths for LEDs used for noninvasive Hb detection ranges from two to nine, while the wavelength range covers substantially 660 nm to 1348 nm [11][12][13][14][15][16][17][18][19]. In fact, the choice of wavelength range is very important.…”

“…At the same time, in order to increase the sensitivity of Hb detection, the wavelength interval should be selected as small as possible in a region where the absorption coefficient changes drastically, and the wavelength interval can be slightly larger in a region where the variation is blunt. erefore, we eventually developed the probe sensor, which consists of eight LEDs with wavelengths from 610 nm to 940 nm, is capable of deriving eight channels of PPG signals with sufficiently high speed, and is different from previous studies in the choice of wavelength [11][12][13][14][15][16][17][18][19].…”

“…PLS regression and ANN have been used for noninvasive blood chemical measurements in recent years. Combined with principal component analysis and BP-ANN, Ding et al obtained an optimal 9-11-1 (number of nodes for input layer, hidden layer, and output layer) network structure and proved that the spectrophotometric system they had developed was able to detect the Hb concentration noninvasively [17]. With extracted time-domain characteristics of PPG signal, Reşit Kavsaoglu et al used eight machine learning methods including PLS regression, generalized regression neural network, and support vector regression for the measurement of Hb level.…”

“…In 2014, with a couple of PPG signals at two different wavelengths, Nirupa and Kumar achieved the measurement of Hb concentration, oxygen saturation, heart rate, and heart rate variability [16]. On the basis of the formed nine wavelengths broadband light source and silicium photodiode array, Ding et al constructed a high-performance spectrophotometric system for noninvasive Hb monitoring with principal component analysis combined with BP-ANN model [17]. With eight regression machine learning algorithms, Reşit Kavsaoglu proposed a noninvasive method for the prediction of the Hb values using the characteristic features of the PPG signals and the results were promising [18].…”

Classification of mild cognitive impairment and Alzheimer’s Disease with machine-learning techniques using 1H Magnetic Resonance Spectroscopy data

“…This technique is based on the simulation of the human cognition process (McCulloch and Pitts, 1990). ANNs have been widely used for different problems, such as data analysis, non-invasive diagnosis in the medical field (Ding et al, 2014;S. Resino et al, 2011), linking chemical knowledge (GomezCarracedo et al, 2007;Gómez-Carracedo et al, 2007) to protein function prediction (C. Fernandez-Lozano et al, 2013a, 2014a, 2014b or, as mentioned before, for AD classification with morphometric measures (Aguilar et al, 2013;Escudero et al, 2011).…”