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Diabetes mellitus and its complication such as heart disease, stroke, kidney failure, etc. is a serious concern all over the world. Hence, monitoring some important blood parameters non-invasively is of utmost importance, that too with high accuracy. This paper presents an in-house developed system, which will be helpful for diabetes patients with Chronic Kidney Disease (CKD) to monitor blood urea and glucose. This manuscript discusses a comparative study for the prediction of blood urea and glucose using Backpropagation Artificial Neural Network (BP-ANN) and Partial Least Square Regression (PLSR) model. The NVIDIA Jetson Nano board controls the five fixed LED wavelengths in the Near Infrared (NIR) region from 2.0 µm to 2.5 µm with a constant emission power of 1.2 mW. The spectra for 57 laboratory prepared samples conforming with major blood constituents of the blood sample were recorded. From these samples, 53 spectra were used for training/calibration of the BP-ANN/PLSR model and the remaining 4 samples were used for validating the model. The PLSR model predicts blood urea and glucose with a Root Mean Square Error (RMSE) of 0.88 & 12.01 mg/dL, Coefficient of Determination R 2 =0.93 & R 2 =0.97, Accuracy of 94.2 % and 90.14 %, respectively. To improve the prediction accuracy, BP-ANN model is applied. Later the Principal Component Analysis (PCA) technique was applied to these 57 spectra values. These PCA values were used to train and validate the BP-ANN model. After applying the BP-ANN model, the prediction of blood urea & glucose improved remarkably, which achieved RMSE of 0.69 mg/dL, R 2 =0.96, Accuracy of 95.96 % for urea and RMSE of 2.06 mg/dL R 2 =0.99, and Accuracy of 98.65 % for glucose. The system performance is then evaluated with Bland Altman analysis and Clarke Error Grid Analysis (CEGA).

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Nowadays, hemoglobin monitoring is essential during surgeries, blood donations, and dialysis. Which are normally done using invasive methods. To monitor hemoglobin, a non-invasive hemoglobin meter was developed with five fixed light-emitting diode (LED) wavelengths at 670 nm, 770 nm, 810 nm, 850 nm, 950 nm and controlled using an Arduino Uno embedded development board. A photodetector with an on-chip trans-impedance amplifier was utilized to acquire the transmitted signal through the finger using the photoplethysmography (PPG) principle. Before the standardization of LED power, we had tested the designed system on fifteen subjects for the five wavelengths and estimated the hemoglobin with an accuracy of 96.51% and root mean square error (RMSE) of 0.57 gm/dL. To further improve the accuracy, the LED power was standardized and the PPG signal was reacquired on the same subjects. With this, the accuracy improved to 98.29% and also reduced the RMSE to 0.36 gm/dL. The designed system with LED power standardization showed a good agreement with pathology results with the coefficient of determination R²=0.981. Also, Bland–Altman analysis was used to evaluate the designed system and it showed good agreement between the two measurements.

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