Machine Learning Classification for Assessing the Degree of Stenosis and Blood Flow Volume at Arteriovenous Fistulas of Hemodialysis Patients Using a New Photoplethysmography Sensor Device

Machine Learning Classification for Assessing the Degree of Stenosis and Blood Flow Volume at Arteriovenous Fistulas of Hemodialysis Patients Using a New Photoplethysmography Sensor Device

The classifier of support vector machine (SVM) learning for assessing the quality of arteriovenous fistulae (AVFs) in hemodialysis (HD) patients using a new photoplethysmography (PPG) sensor device is presented in this work. In clinical practice, there are two important indices for assessing the qua...

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Bibliographic Details
Main Authors: Pei-Yu Chiang, Paul C. -P. Chao, Tse-Yi Tu, Yung-Hua Kao, Chih-Yu Yang, Der-Cherng Tarng, Chin-Long Wey
Format: Article
Language:English
Published: MDPI AG 2019-08-01
Series:Sensors
Subjects:
photoplethysmography (PPG) sensor
arteriovenous fistula (AVF)
hemodialysis (HD) patients
machine learning classifiers
support vector machine (SVM)
Online Access:https://www.mdpi.com/1424-8220/19/15/3422

Internet

https://www.mdpi.com/1424-8220/19/15/3422

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