Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu 300, Taiwan.
Division of Nephrology in Taipei Veterans General Hospital, Taipei 112, Taiwan.
Sensors (Basel). 2018 Nov 9;18(11):3854. doi: 10.3390/s18113854.
A portable, wireless photoplethysomography (PPG) sensor for assessing arteriovenous fistula (AVF) by using class-weighted support vector machines (SVM) was presented in this study. Nowadays, in hospital, AVF are assessed by ultrasound Doppler machines, which are bulky, expensive, complicated-to-operate, and time-consuming. In this study, new PPG sensors were proposed and developed successfully to provide portable and inexpensive solutions for AVF assessments. To develop the sensor, at first, by combining the dimensionless number analysis and the optical Beer Lambert's law, five input features were derived for the SVM classifier. In the next step, to increase the signal-noise ratio (SNR) of PPG signals, the front-end readout circuitries were designed to fully use the dynamic range of analog-digital converter (ADC) by controlling the circuitries gain and the light intensity of light emitted diode (LED). Digital signal processing algorithms were proposed next to check and fix signal anomalies. Finally, the class-weighted SVM classifiers employed five different kernel functions to assess AVF quality. The assessment results were provided to doctors for diagonosis and detemining ensuing proper treatments. The experimental results showed that the proposed PPG sensors successfully achieved an accuracy of 89.11% in assessing health of AVF and with a type II error of only 9.59%.
本研究提出了一种用于评估动静脉瘘（AVF）的便携式无线光电容积脉搏波（PPG）传感器，该传感器使用了类别加权支持向量机（SVM）。如今，在医院中，AVF 的评估是通过超声多普勒机器进行的，这些机器体积庞大、昂贵、操作复杂且耗时。在本研究中，成功地提出并开发了新的 PPG 传感器，为 AVF 评估提供了便携式和经济实惠的解决方案。为了开发传感器，首先，通过无量纲数分析和光学 Beer-Lambert 定律，为 SVM 分类器推导出了五个输入特征。下一步，为了提高 PPG 信号的信噪比（SNR），通过控制电路增益和发光二极管（LED）的光强，设计了前端读出电路，以充分利用模数转换器（ADC）的动态范围。接下来提出了数字信号处理算法来检查和修复信号异常。最后，类别加权 SVM 分类器使用了五种不同的核函数来评估 AVF 的质量。评估结果提供给医生进行诊断并确定后续的适当治疗。实验结果表明，所提出的 PPG 传感器在评估 AVF 健康状况方面的准确率达到了 89.11%，而 II 类错误率仅为 9.59%。
