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Topic |
A Machine Learning Approach for Identification of Vascular Access Patency in Hemodialysis Patients Using Photoplethysmography: A pilot study |
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First Author |
Po-Kai Yang, M.D |
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Order of Authors: |
Danyal Shahmirzadi ; Hong-Xu Zhuo ; Chuan-Yu Chang ; Chin-Chung Tseng ; Wen-Fong Wang( Corresponding Author) |
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Abstract |
Introduction: Vascular access (VA) is essential for
patients with hemodialysis, and its dysfunction is a major complication that
can reduce quality of life or even threaten life. VA
patency is not only difficult to predict on an individual basis,
but also challenging to predict in real-time. To overcome this challenge,
this study aimed to develop a machine learning approach to predict 6-month primary patency (PP) using photoplethysmography (PPG) signals
acquired from the tips of both index fingers. Materials and Methods: PPG
signals were obtained from hemodialysis patients who
received an arteriovenous fistula or an arteriovenous graft as primary VA in
a single center from April 2023 to December 2023.
With PPG wearables, we propose a method that can efficiently and
quickly generate the morphological features of the PPG signal to recognize
different groups of patients. For the generated features, an
independent sample t-test was used to evaluate their effectiveness for
machine learning. Then, two supervised learning algorithms,
k-nearest neighbors (kNN) and support vector machine (SVM), are used further
to identify VA patency
in advance. Results:
The study involved 31 patients, of
whom 14 had 6-month PP, while 17 did not. Using
the kNN algorithm, machine learning classified patients into two groups with 82% precision based on PPG signals, while the SVM
algorithm showed a precision of 82%. Conclusions:
Our approach can provide reliable classifications
for VA patency. It is effective to use the proposed PPG signal features to
predict 6-month PP of VA. |
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Keywords |
Hemodialysis, vascular access,
photoplethysmography, autocorrelation, cross-correlation, machine learning |
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download |
Data for Machine Learning、 kNN、SVM |
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