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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Detalles Bibliográficos
Autores principales: Chiang, Pei-Yu, Chao, Paul C. -P., Tu, Tse-Yi, Kao, Yung-Hua, Yang, Chih-Yu, Tarng, Der-Cherng, Wey, Chin-Long
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6695851/
https://www.ncbi.nlm.nih.gov/pubmed/31382707
http://dx.doi.org/10.3390/s19153422

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6695851/
https://www.ncbi.nlm.nih.gov/pubmed/31382707
http://dx.doi.org/10.3390/s19153422

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