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

Sensors

Yung-Hua Kao¹  Chin-Long Wey¹  Der-Cherng Tarng²  Chih-Yu Yang²  PaulC. -P. Chao³  Tse-Yi Tu³  Pei-Yu Chiang³ 

[1] Department of Electrical Engineering, National Chiao Tung University, Hsinchu 300, Taiwan;Division of Nephrology in Taipei Veterans General Hospital, Taipei 11217, Taiwan;Institute of Electrical Control Engineering, National Chiao Tung University, Hsinchu 300, Taiwan;

关键词: photoplethysmography (PPG) sensor;    arteriovenous fistula (AVF);    hemodialysis (HD) patients;    machine learning classifiers;    support vector machine (SVM);   

DOI  :  10.3390/s19153422

来源: DOAJ

【 摘 要 】

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 quality of AVF: the blood flow volume (BFV) and the degree of stenosis (DOS). In hospitals, the BFV and DOS of AVFs are nowadays assessed using an ultrasound Doppler machine, which is bulky, expensive, hard to use, and time consuming. In this study, a newly-developed PPG sensor device was utilized to provide patients and doctors with an inexpensive and small-sized solution for ubiquitous AVF assessment. The readout in this sensor was custom-designed to increase the signal-to-noise ratio (SNR) and reduce the environment interference via maximizing successfully the full dynamic range of measured PPG entering an analog−digital converter (ADC) and effective filtering techniques. With quality PPG measurements obtained, machine learning classifiers including SVM were adopted to assess AVF quality, where the input features are determined based on optical Beer−Lambert’s law and hemodynamic model, to ensure all the necessary features are considered. Finally, the clinical experiment results showed that the proposed PPG sensor device successfully achieved an accuracy of 87.84% based on SVM analysis in assessing DOS at AVF, while an accuracy of 88.61% was achieved for assessing BFV at AVF.

【 授权许可】

Unknown