Achieving High Accuracy in Predicting Blood Flow Volume at the Arteriovenous Fistulas of Hemodialysis Patients by Intelligent Quality Assessment on PPGs

Abstract

A machine learning (ML) algorithm is successfully developed to assess the signal quality of measured photoplethysmography (PPG) waveforms for effective real-time prediction of blood flow volume (BFV). This algorithm is essential to achieve high prediction accuracy of BFV for hemodialysis patients to monitor the quality of their arteriovenous fistulas (AVFs) at home by themselves using a new hand-held device. The algorithm is built on an ML classifier of 1-dimensional convolutional neural network (1D-CNN), calibrated by two groups of PPG waveforms that are pre-identified by experienced experts to 300 qualified and 202 un-qualified PPG waveforms in 6-second windows, as the ones to render accurate BFV predictions and the others to inaccurates, respectively. The qualified ones satisfy at least the deterministic criteria such as adequate signal-to-noise-ratio (SNR), stable/small fluctuations of AC/DC components, and also the presence of secondary bio-features, to ensure minimized influence from mis-positioning, hand movement, pressurization and varied ambient lighting. With the classifier algorithm in hand, together with another fully-connected neural network for estimating BFV, a combined real-time computation algorithm is built, being able to achieve much better accuracy for real-time measurement ubiquitously. In results, using the newly-developed quality-assessment algorithm, the error of predicted BFVs is improved from ±175.577 ml/min to ±122.8259 ml/min significantly.