Next Article in Journal
Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression
Previous Article in Journal
An Adaptive Prediction Target Search Algorithm for Multi-AUVs in an Unknown 3D Environment
Article Menu

Export Article

Open AccessArticle
Sensors 2018, 18(11), 3854;

A Portable, Wireless Photoplethysomography Sensor for Assessing Health of Arteriovenous Fistula Using Class-Weighted Support Vector Machine

Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu 300, Taiwan
Division of Nephrology in Taipei Veterans General Hospital, Taipei 112, Taiwan
Author to whom correspondence should be addressed.
Received: 27 August 2018 / Revised: 28 October 2018 / Accepted: 6 November 2018 / Published: 9 November 2018
(This article belongs to the Section Biosensors)
Full-Text   |   PDF [3948 KB, uploaded 9 November 2018]   |  


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%. View Full-Text
Keywords: photoplethysmography (PPG) sensor; support vector machine (SVM); arteriovenous fistula (AVF) photoplethysmography (PPG) sensor; support vector machine (SVM); arteriovenous fistula (AVF)

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Chao, P. .-P.; Chiang, P.-Y.; Kao, Y.-H.; Tu, T.-Y.; Yang, C.-Y.; Tarng, D.-C.; Wey, C.-L. A Portable, Wireless Photoplethysomography Sensor for Assessing Health of Arteriovenous Fistula Using Class-Weighted Support Vector Machine. Sensors 2018, 18, 3854.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top