Abstract
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.
Highlights
The arteriovenous fistula (AVF), which refers to the surgical connection between an artery and a vein at the forearm, is the lifeline of chronic kidney disease (CKD) patients for performing hemodialysis (HD) treatment
The confusion matrix of the classification results of assessing degree of stenosis (DOS) and blood flow volume (BFV) are shown in Tables 2 and 3, employed PPG sensor device with the designed support vector machine (SVM) classifier showed better performance compared respectively, where it can be seen that the SVM classifier showed higher accuracies and lower type II
A newly developed PPG sensor device with a SVM classifier designed for assessing DOS and BFV at AVF were proposed in this work
Summary
The arteriovenous fistula (AVF), which refers to the surgical connection between an artery and a vein at the forearm, is the lifeline of chronic kidney disease (CKD) patients for performing hemodialysis (HD) treatment. Convenient measurements, there were some published works devoted to developing small-sized sensors for evaluating DOS and/or BVF non-invasively. Wu et al [6] presented a bilateral PPG sensor system to evaluate DOS at AVF by a cooperative game algorithm, which results in a correlation greater than 0.9. Du and Stephanus [7,8] published works assessing DOS at AVF using bilateral PPG sensors and achieved 94.82% in accuracy. A single, small-sized, hand-held PPG sensor [10] was utilized for assessing DOS at AVF based on measured PPG waveforms. Chiang et al [10] presented a single, newly-developed PPG sensor system to monitor and quantify the BFV in AVF with a resulting correlation of 71.76%.
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