Plantar Planar Dual-Array Electrical Impedance Tomography (PPDA-EIT) Method for Early Diabetic Foot Ulcers’ Detection

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Yunqian Wang;Hui Feng;Bo Sun;Yuru Bai;Songpei Hu;Jiafeng Yao;Tong Zhao
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Abstract

The relationship between impedance and the concentrations of glucose and L-tyrosine (TYR) has been clarified by the plantar planar dual-array electrical impedance tomography (PPDA-EIT) for early detection of diabetic foot ulcers (DFUs). PPDA-EIT images the electrical response changes of glucose and L-TYR at different concentrations in the DFU environment. In the experiments, the characteristic frequency was determined at the maximum impedance difference ( $\Delta $ Z) by the electrochemical impedance spectroscopy (EIS) method. Optimal sensor location and size were determined based on analyzing the pressure concentration area during gait. The optimum electrode size and excitation method were found through 3-D electromagnetic simulation. Pork-mimic experiments were conducted to validate the sensor design by evaluating the image correlation coefficient (ICC) and the root mean square error (RMSE). From the EIS experimental results, glucose and L-TYR, as biomarkers for early DFUs’ detection, produced the largest impedance difference ( $\Delta $ Z) at a measurement frequency of 1 MHz. From the 3-D electromagnetic simulation results, the most effective excitation involved the central electrode (sixth electrode), with the optimal electrode being a square with a side length of ${D} = 8$ mm. The PPDA-EIT sensor was able to satisfactorily obtain reconstrued images of the object with an optimal ICC =0.8110 and RMSE =0.0982, which suggests that the approach used in this study provides an accurate indication of the glucose and L-TYR concentration change region in early DFUs.
用于早期糖尿病足溃疡检测的足底平面双阵列电阻抗断层扫描(PPDA-EIT)方法
用于早期检测糖尿病足溃疡(DFU)的足底平面双阵列电阻抗断层成像(PPDA-EIT)明确了阻抗与葡萄糖和左旋酪氨酸(TYR)浓度之间的关系。PPDA-EIT 对 DFU 环境中不同浓度葡萄糖和 L-TYR 的电响应变化进行成像。在实验中,通过电化学阻抗谱(EIS)方法确定了最大阻抗差($\Delta $ Z)处的特征频率。根据对步态过程中压力集中区域的分析,确定了最佳传感器位置和尺寸。通过三维电磁模拟找到了最佳电极尺寸和激励方法。通过评估图像相关系数(ICC)和均方根误差(RMSE),进行了猪肉模拟实验,以验证传感器的设计。从 EIS 实验结果来看,葡萄糖和 L-TYR 作为早期 DFU 检测的生物标记物,在测量频率为 1 MHz 时产生的阻抗差 ( $\Delta $ Z) 最大。从三维电磁模拟结果来看,最有效的激励涉及中央电极(第六电极),最佳电极是边长为 ${D} = 8$ mm 的正方形。PPDA-EIT 传感器能够以最佳 ICC =0.8110 和 RMSE =0.0982 获得令人满意的物体重构图像,这表明本研究采用的方法能够准确显示早期 DFU 的葡萄糖和 L-TYR 浓度变化区域。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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