Bilateral photoplethysmography for peripheral arterial disease screening in haemodialysis patients using astable multivibrator and machine learning classifier. Issue 9 (1st November 2019)

• Record Type: Journal Article
• Authors: Wu, Jian‐Xing; Lin, Chia‐Hung; Kan, Chung‐Dann; Chen, Wei‐Ling
• Is Part Of: IET science, measurement & technology. Volume 13:Issue 9(2019)
• Page Start: 1277
• Page End: 1286
• Publication Date: 2019-11-01
• Subjects: learning (artificial intelligence) -- diseases -- medical signal processing -- photoplethysmography -- haemodynamics -- blood vessels -- probability; peripheral arterial disease screening -- haemodialysis patients -- astable multivibrator -- machine learning classifier -- type 2 diabetes -- upper peripheral arteries -- progressive dialysis access stenosis -- HD patients -- noninvasive bilateral photoplethysmography -- continuous variations -- blood flow volume -- peripheral circulation system -- PPG oscillation -- time constants -- duty ratio -- rising time -- amplitude ratio -- systolic pressures -- diastolic pressures -- bilateral differences -- screening method -- probability values -- PAD screening -- bilateral timing parameters -- in vivo examinations -- atherosclerosis -- lower peripheral arteries -- machine learning decision‐making process -- radial‐based function -- colour relation analysis -- perceptual colour relationships -- resistive index -- embedded system; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2018.5330
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : Peripheral arterial disease (PAD) is highly prevalent in haemodialysis (HD) patients with type 2 diabetes. Atherosclerosis may occur in both lower and upper peripheral arteries, causing progressive dialysis access stenosis in HD patients. To assess the risk of PAD, non‐invasive bilateral photoplethysmography (PPG) can be used to obtain continuous variations in blood flow volume in in vivo examinations. The authors propose an astable multivibrator to model the peripheral circulation system and to produce PPG oscillation with time constants, duty ratio (rising time), and amplitude ratio of systolic and diastolic pressures. Then, the bilateral differences in the time constant and duty ratio are used to separate the normal condition from PAD. The machine learning decision‐making process utilises a screening method to automatically detect subjects with and without the risk of PAD. The radial‐based function is employed to parameterise the similarity and dissimilarity levels using probability values. Colour relation analysis incorporates the probability values into the perceptual colour relationships for PAD screening. The experimental results indicate that in comparison with bilateral timing parameters, degree of stenosis, and resistive index, the proposed screening method is efficient in preventing complications of PAD and is easily implemented in an embedded system.