Md Rezwanul Haque

Blood components such as hemoglobin, glucose, and creatinine are essential for monitoring one’s health condition. The current blood component measurement approaches still depend on invasive techniques that are painful and uncomfortable for patients. To facilitate measurement at home, we proposed a novel non-invasive technique to measure blood hemoglobin, glucose, and creatinine levels based on Photoplethysmography (PPG) signals using Deep Neural Networks (DNN). Fingertip videos from 93 subjects have been collected using a smartphone. The PPG signal is generated from each video, and 46 characteristic features are then extracted from the PPG signal, its derivatives (1st and 2nd), and from Fourier analysis. Additionally, age and gender are also included as features due to their significant effects on hemoglobin, glucose, and creatinine. A correlation-based feature selection (CFS) using genetic algorithms (GA) has been used to select the optimal features to avoid redundancy and overfitting. Finally, DNN-based models have been developed to estimate the blood Hemoglobin (Hb), Glucose (Gl), and Creatinine (Cr) levels from the selected features. The approach provides the best-estimated accuracy of R² = 0.922 for Hb, R² = 0.902 for Gl, and R² = 0.969 for Cr. Experimental results show that the proposed method is a suitable technique to be used clinically to measure human blood component levels without taking blood samples. This paper also reveals that smartphone-based PPG signals have great potential to measure different blood components.