Photoplethysmography Analysis with Duffing–Holmes Self-Synchronization Dynamic Errors and 1D CNN-Based Classifier for Upper Extremity Vascular Disease Screening

Common upper limb peripheral artery diseases (PADs) are atherosclerosis, embolic diseases, and systemic diseases, which are often asymptomatic, and the narrowed arteries (stenosis) will gradually reduce blood flow in the right or left upper limbs. Upper extremity vascular disease (UEVD) and atherosc...

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Autores principales:	Pi-Yun Chen, Zheng-Lin Sun, Jian-Xing Wu, Ching-Chou Pai, Chien-Ming Li, Chia-Hung Lin, Neng-Sheng Pai
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	upper extremity vascular diseases wrist-based photoplethysmography Duffing–Holmes system 1D convolutional neural network Chemical technology TP1-1185 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/a55913184e4a42f3a3be6387c7aa0574
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spelling	oai:doaj.org-article:a55913184e4a42f3a3be6387c7aa05742021-11-25T18:52:18ZPhotoplethysmography Analysis with Duffing–Holmes Self-Synchronization Dynamic Errors and 1D CNN-Based Classifier for Upper Extremity Vascular Disease Screening10.3390/pr91120932227-9717https://doaj.org/article/a55913184e4a42f3a3be6387c7aa05742021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9717/9/11/2093https://doaj.org/toc/2227-9717Common upper limb peripheral artery diseases (PADs) are atherosclerosis, embolic diseases, and systemic diseases, which are often asymptomatic, and the narrowed arteries (stenosis) will gradually reduce blood flow in the right or left upper limbs. Upper extremity vascular disease (UEVD) and atherosclerosis are high-risk PADs for patients with Type 2 diabetes or with both diabetes and end-stage renal disease. For early UEVD detection, a fingertip-based, toe-based, or wrist-based photoplethysmography (PPG) tool is a simple and noninvasive measurement system for vital sign monitoring and healthcare applications. Based on time-domain PPG analysis, a Duffing–Holmes system with a master system and a slave system is used to extract self-synchronization dynamic errors, which can track the differences in PPG morphology (in amplitudes (systolic peak) and time delay (systolic peak to diastolic peak)) between healthy subjects and PAD patients. In the preliminary analysis, the self-synchronization dynamic errors can be used to evaluate risk levels based on the reflection index (RI), which includes normal condition, lower PAD, and higher PAD. Then, a one-dimensional convolutional neural network is established as a multilayer classifier for automatic UEVD screening. The experimental results indicated that the self-synchronization dynamic errors have a positive correlation with the RI (<i>R</i><sup>2</sup> = 0.6694). The <i>K</i>-fold cross-validation is used to verify the performance of the proposed classifier with recall (%), precision (%), accuracy (%), and <i>F</i><sub>1</sub> score.Pi-Yun ChenZheng-Lin SunJian-Xing WuChing-Chou PaiChien-Ming LiChia-Hung LinNeng-Sheng PaiMDPI AGarticleupper extremity vascular diseaseswrist-based photoplethysmographyDuffing–Holmes system1D convolutional neural networkChemical technologyTP1-1185ChemistryQD1-999ENProcesses, Vol 9, Iss 2093, p 2093 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	upper extremity vascular diseases wrist-based photoplethysmography Duffing–Holmes system 1D convolutional neural network Chemical technology TP1-1185 Chemistry QD1-999
spellingShingle	upper extremity vascular diseases wrist-based photoplethysmography Duffing–Holmes system 1D convolutional neural network Chemical technology TP1-1185 Chemistry QD1-999 Pi-Yun Chen Zheng-Lin Sun Jian-Xing Wu Ching-Chou Pai Chien-Ming Li Chia-Hung Lin Neng-Sheng Pai Photoplethysmography Analysis with Duffing–Holmes Self-Synchronization Dynamic Errors and 1D CNN-Based Classifier for Upper Extremity Vascular Disease Screening
description	Common upper limb peripheral artery diseases (PADs) are atherosclerosis, embolic diseases, and systemic diseases, which are often asymptomatic, and the narrowed arteries (stenosis) will gradually reduce blood flow in the right or left upper limbs. Upper extremity vascular disease (UEVD) and atherosclerosis are high-risk PADs for patients with Type 2 diabetes or with both diabetes and end-stage renal disease. For early UEVD detection, a fingertip-based, toe-based, or wrist-based photoplethysmography (PPG) tool is a simple and noninvasive measurement system for vital sign monitoring and healthcare applications. Based on time-domain PPG analysis, a Duffing–Holmes system with a master system and a slave system is used to extract self-synchronization dynamic errors, which can track the differences in PPG morphology (in amplitudes (systolic peak) and time delay (systolic peak to diastolic peak)) between healthy subjects and PAD patients. In the preliminary analysis, the self-synchronization dynamic errors can be used to evaluate risk levels based on the reflection index (RI), which includes normal condition, lower PAD, and higher PAD. Then, a one-dimensional convolutional neural network is established as a multilayer classifier for automatic UEVD screening. The experimental results indicated that the self-synchronization dynamic errors have a positive correlation with the RI (<i>R</i><sup>2</sup> = 0.6694). The <i>K</i>-fold cross-validation is used to verify the performance of the proposed classifier with recall (%), precision (%), accuracy (%), and <i>F</i><sub>1</sub> score.
format	article
author	Pi-Yun Chen Zheng-Lin Sun Jian-Xing Wu Ching-Chou Pai Chien-Ming Li Chia-Hung Lin Neng-Sheng Pai
author_facet	Pi-Yun Chen Zheng-Lin Sun Jian-Xing Wu Ching-Chou Pai Chien-Ming Li Chia-Hung Lin Neng-Sheng Pai
author_sort	Pi-Yun Chen
title	Photoplethysmography Analysis with Duffing–Holmes Self-Synchronization Dynamic Errors and 1D CNN-Based Classifier for Upper Extremity Vascular Disease Screening
title_short	Photoplethysmography Analysis with Duffing–Holmes Self-Synchronization Dynamic Errors and 1D CNN-Based Classifier for Upper Extremity Vascular Disease Screening
title_full	Photoplethysmography Analysis with Duffing–Holmes Self-Synchronization Dynamic Errors and 1D CNN-Based Classifier for Upper Extremity Vascular Disease Screening
title_fullStr	Photoplethysmography Analysis with Duffing–Holmes Self-Synchronization Dynamic Errors and 1D CNN-Based Classifier for Upper Extremity Vascular Disease Screening
title_full_unstemmed	Photoplethysmography Analysis with Duffing–Holmes Self-Synchronization Dynamic Errors and 1D CNN-Based Classifier for Upper Extremity Vascular Disease Screening
title_sort	photoplethysmography analysis with duffing–holmes self-synchronization dynamic errors and 1d cnn-based classifier for upper extremity vascular disease screening
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a55913184e4a42f3a3be6387c7aa0574
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Photoplethysmography Analysis with Duffing–Holmes Self-Synchronization Dynamic Errors and 1D CNN-Based Classifier for Upper Extremity Vascular Disease Screening

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