Non-invasive prediction of hemoglobin level using machine learning techniques with the PPG signal's characteristics features

• Published in: Applied soft computing Vol. 37; pp. 983 - 991
• Main Authors: Kavsaoğlu, A. Reşit, Polat, Kemal, Hariharan, M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2015
• Subjects: Feature extraction; Feature selection; Hemoglobin (Hb); Photoplethysmography (PPG); Prediction; Regression models; Feature extraction; Regression models; Feature selection; Hemoglobin (Hb); Photoplethysmography (PPG); Prediction
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2015.04.008

Hemoglobin level measurement device based on PPG signal. •A prediction algorithm method is developed by entering the characteristic features of the PPG signal into machine learning algorithms in order to measure the hemoglobin value non-invasively.•We have used the PPG signals in the prediction of hemoglobin levels.•As the machine learning algorithms, CART (classification and regression trees), LSR (least squares regression), GLR (generalized linear regression), MVLR (multivariate linear regression), PSLR (partial least squares regression), GRNN (generalized regression neural network), MLP (multilayer perceptron) and SVR (support vector regression) are used.•The methods of RELIEFF feature selection (RFS) and correlation-based feature selection (CFS) are used to decrease the number of features. Hemoglobin can be measured normally after the analysis of the blood sample taken from the body and this measurement is named as invasive. Hemoglobin must continuously be measured to control the disease and its progression in people who go through hemodialysis and have diseases such as oligocythemia and anemia. This gives a perpetual feeling of pain to the people. This paper proposes a non-invasive method for the prediction of the hemoglobin using the characteristic features of the PPG signals and different machine learning algorithms. In this work, PPG signals from 33 people were included in 10 periods and 40 characteristic features were extracted from them. In addition to these features, gender information (male or female), height (as cm), weight (as kg) and age of each subjects were also considered as the features. Blood count and hemoglobin level were measured simultaneously by using the “Hemocue Hb-201TM” device. Using the different machine learning regression techniques (classification and regression trees – CART, least squares regression – LSR, generalized linear regression – GLR, multivariate linear regression – MVLR, partial least squares regression – PLSR, generalized regression neural network – GRNN, MLP – multilayer perceptron, and support vector regression – SVR). RELIEFF feature selection (RFS) and correlation-based feature selection (CFS) were used to select the best features. Original features and selected features using RFS (10 features) and CFS (11 features) were used to predict the hemoglobin level using the different machine learning techniques. To evaluate the performance of the machine learning techniques, different performance measures such as mean absolute error – MAE, mean square error – MSE, R2 (coefficient of determination), root mean square error – RMSE, Mean Absolute Percentage Error (MAPE) and Index of Agreement – IA were used. The promising results were obtained (MSE-0.0027) using the selected features by RFS and SVR. Hence, the proposed method may clinically be used to predict the hemoglobin level of human being clinically without taking and analyzing blood samples.