標題: 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
作者: Chiang, Pei-Yu
Chao, Paul C. -P.
Tu, Tse-Yi
Kao, Yung-Hua
Yang, Chih-Yu
Tarng, Der-Cherng
Wey, Chin-Long
電子工程學系及電子研究所
電控工程研究所
Department of Electronics Engineering and Institute of Electronics
Institute of Electrical and Control Engineering
關鍵字: photoplethysmography (PPG) sensor;arteriovenous fistula (AVF);hemodialysis (HD) patients;machine learning classifiers;support vector machine (SVM)
公開日期: 1-八月-2019
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.
URI: http://dx.doi.org/10.3390/s19153422
http://hdl.handle.net/11536/152837
DOI: 10.3390/s19153422
期刊: SENSORS
Volume: 19
Issue: 15
起始頁: 0
結束頁: 0
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