Aminah Hina - Academia.edu (original) (raw)

Papers by Aminah Hina

IEEE Conference Proceedings, 2019

IEEE Sensors Journal, Jul 15, 2022

Sensors, Jun 27, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

2022 20th IEEE Interregional NEWCAS Conference (NEWCAS), Jun 19, 2022

Sensors

The past few decades have seen ongoing development of continuous glucose monitoring (CGM) systems... more The past few decades have seen ongoing development of continuous glucose monitoring (CGM) systems that are noninvasive and accurately measure blood glucose levels. The conventional finger-prick method, though accurate, is not feasible for use multiple times a day, as it is painful and test strips are expensive. Although minimally invasive and noninvasive CGM systems have been introduced into the market, they are expensive and require finger-prick calibrations. As the diabetes trend is high in low- and middle-income countries, a cost-effective and easy-to-use noninvasive glucose monitoring device is the need of the hour. This review paper briefly discusses the noninvasive glucose measuring technologies and their related research work. The technologies discussed are optical, transdermal, and enzymatic. The paper focuses on Near Infrared (NIR) technology and NIR Photoplethysmography (PPG) for blood glucose prediction. Feature extraction from PPG signals and glucose prediction with mach...

2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), 2014

ABSTRACT

IEEE Transactions on Biomedical Circuits and Systems, 2020

Conventional glucose monitoring methods for the growing numbers of diabetic patients around the w... more Conventional glucose monitoring methods for the growing numbers of diabetic patients around the world are invasive, painful, costly and, time-consuming. Complications aroused due to the abnormal blood sugar levels in diabetic patients have created the necessity for continuous noninvasive glucose monitoring. This article presents a wearable system for glucose monitoring based on a single wavelength near-infrared (NIR) Photoplethysmography (PPG) combined with machine-learning regression (MLR). The PPG readout circuit consists of a switched capacitor Transimpedance amplifier with 1 MΩ gain and a 10-Hz switched capacitor LPF. It allows a DC bias current rejection up to 20 μA with an input-referred current noise of 7.3 pA/√Hz. The proposed digital processor eliminates motion artifacts, and baseline drifts from PPG signal, extracts six distinct features and finally predicts the blood glucose level using Support Vector Regression with Fine Gaussian kernel (FGSVR) MLR. A novel piece-wise linear (PWL) approach for the exponential function is proposed to realize the FGSVR on-chip. The overall system is implemented using a 180 nm CMOS process with a chip area of 4.0 mm2 while consuming 1.62 mW. The glucose measurements are performed for 200 subjects with R2 of 0.937. The proposed system accurately predicts the sugar level with a mean absolute relative difference (mARD) of 7.62%.

$Research paper thumbnail of A <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>186</mn><mi>μ</mi><mi mathvariant="normal">W</mi></mrow><annotation encoding="application/x-tex">186\mu \mathrm{W}</annotation></semantics></math>186μW Glucose Monitoring SoC using Near-Infrared Photoplethysmography$

2020 IEEE Asian Solid-State Circuits Conference (A-SSCC)

A glucose monitoring SoC based on near-infrared Photoplethysmography (PPG) is presented. It integ... more A glucose monitoring SoC based on near-infrared Photoplethysmography (PPG) is presented. It integrates fully differential AFE with nonlinear support-vector-machine regression (NSVMR). The AFE utilizes chopper to reduce input-referred current noise by 57%, and trans-impedance amplifier input impedance by 90%, thus allows 115dB dynamic range. The NSVMR is realized using the piecewise linear floating-point exponential, which decreases the area by 27% compared to conventional implementations. The 6mm2 SoC in 0.18um CMOS consumes 186mumathrmW186 \mu \mathrm{W}186mumathrmW and reduces the mean absolute relative difference (mARD) by 30% verified on 200 subjects.

2021 1st International Conference on Microwave, Antennas & Circuits (ICMAC)

IEEE International Symposium on Circuits and Systems proceedings, 2019

This live demonstration showcases a noninvasive glucose monitoring device based on a single wavel... more This live demonstration showcases a noninvasive glucose monitoring device based on a single wavelength near-infrared (NIR) spectroscopy. The analog frontend record the Photoplethysmographic (PPG) signal from the fingertip. Ten discriminating features are extracted from the PPG signal to predict the blood glucose level using (Exponential Gaussian Process) machine learning regression. Visitors can easily insert their fingers into the finger-clip part of the system to measure their blood glucose level and display it on a mobile phone.

2019 IEEE International Symposium on Circuits and Systems (ISCAS)

Continuous glucose monitoring is essential for patients to avoid complications of both hypoglycem... more Continuous glucose monitoring is essential for patients to avoid complications of both hypoglycemia and hyperglycemia. This paper presents a novel non-invasive continuous blood glucose monitoring system based on a single wavelength near-infrared (NIR) spectroscopy. The analog frontend of the system is designed with a single NIR LED to record the Photoplethysmographic (PPG) signal from the fingertip with motion artifacts removal and a bias current rejection up to 20uA. The proposed digital backend extracts 10 discriminating features from the PPG signal to predict the blood glucose level using (Exponential Gaussian Process) machine learning regression. To realize the feature extraction on FPGA, a novel two-dimensional structure of 256-point Fast Fourier Transform (FFT) is implemented which achieves a 47% reduction in complex multiplications compared to the conventional Radix-2 algorithm. The performance of the proposed system is validated using 200 patients PPG recordings and glucose levels measured via a commercial glucometer. It successfully predicts the glucose level with a mean absolute relative difference (mARD) of 8.97%.

2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)