高血压的存在可能会对使用光容积脉搏波二阶导数无创检测糖尿病造成潜在的缺陷

IF 1.3 Q4 ENGINEERING, BIOMEDICAL
Ahmet Taş, Yaren Alan, Ilke Kara, Abdullah Savas, Muhammed Ikbal Bayhan, Diren Ekici, Zeynep Atay, Fatih Sezer, Cagla Kitapli, Sabahattin Umman, Murat Sezer
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引用次数: 0

摘要

来自光体积脉搏波(PPG)的数据作为检测糖尿病(DM)的无创数字生物标志物显示出有希望的结果。考虑到相互内皮损伤导致类似的不良周围血流动力学扰动,高血压(HT)可能会削弱这种分类性能。二阶导数PPG (SD-PPG)指数由PPG信号的二阶导数推导而来。感兴趣的变量是先前描述的初始正(a),早期负(b),再增加(c),晚期再减少(d),舒张期正(e)和负(f)波的峰值以及它们之间的比值。根据2型糖尿病和高血压表型对患者进行分类。比较患病亚组与健康对照组之间的SD-PPG指数,并评价二分类性能。健康受试者(n = 106)中,2个SDPPG指数b/a比值和血管老化指数(VAI = (b-c-d-e)/a)对分离的2型糖尿病(n = 29)有反应(曲线下面积(AUC)分别为0.629 p = 0.034和0.631 p = 0.031 20)。然而,随着HT患者(n=30)的纳入,分类效果变得不显著。(p = 0.839对p = 0.656)。这些结果提示,HT和DM的共存可能会阻碍SD-PPG在无创DM检测中的应用。关键词:二阶导数光容积脉搏图糖尿病无创心血管筛查指尖波形高血压缩写体重指数= bmi糖尿病= dm糖尿病2型= dm舒张压= db高血压= ht光容积脉搏图= ppg光容积脉搏图二阶导数血管老化指数=血管收缩压= sbp披露声明作者未报告潜在的利益冲突。研究概念和设计由Ahmet Tas提出。所有作者(Ahmet Tas, Yaren Alan, Ilke Kara, Abdullah Savas, Muhammed Ikbal Bayhan, Diren Ekici, Zeynep Atay, Fatih Sezer, Cagla Kitapli, Sabahattin Umman, Murat Sezer)都为材料准备,数据收集和分析(信号和/或统计和/或智力)以及结果解释做出了贡献。手稿的初稿由Ahmet Tas撰写,所有作者(Ahmet Tas, Yaren Alan, Ilke Kara, Abdullah Savas, Muhammed Ikbal Bayhan, Diren Ekici, Zeynep Atay, Fatih Sezer, Cagla Kitapli, Sabahattin Umman, Murat Sezer)都对手稿的前几个版本进行了评论。所有作者(Ahmet Tas, Yaren Alan, Ilke Kara, Abdullah Savas, Muhammed Ikbal Bayhan, Diren Ekici, Zeynep Atay, Fatih Sezer, Cagla Kitapli, Sabahattin Umman, Murat Sezer)阅读并批准了最终手稿。伦理审批数据来源于《自然科学数据》(https://www.nature.com/articles/sdata201820#)上发表的开放数据集。数据收集已在数据描述文章中得到伦理批准,所有参与者已根据开放数据描述文章给予书面同意。所有作者均符合ICMJE的作者资格标准。其他信息资金:作者报告没有与本文所述工作相关的资金。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Presence of hypertension might pose a potential pitfall in detection of diabetes mellitus non-invasively using the second derivative of photoplethysmography
ABSTRACTIndices derived from photoplethysmography (PPG) have shown promising results as non-invasive digital biomarkers for the detection of diabetes mellitus (DM). Considering the mutual endothelial insult leading to similar undesirable peripheral hemodynamic perturbations, hypertension (HT) may blunt this classification performance. Second derivative PPG (SD-PPG) indices were derived from the second derivative of the PPG signal. The variables of interest were the previously described peaks of the initial positive (a), early negative (b), re-increasing (c), late re-decreasing (d), diastolic positive (e) and negative (f) waves and the ratios between them. Patients were classified according to their type 2 DM and hypertension phenotypes. SD-PPG indices were compared between diseased subgroups, healthy controls and also dichotomous classification performance was evaluated. Two SDPPG indices, b/a ratio and the vascular ageing index (VAI = (b-c-d-e)/a) responded to isolated DM type 2 (n = 29) amongst healthy subjects (n = 106) (area under the curve (AUC) = 0.629 p = 0.034 and 0.631 p = 0.031 20 respectively). However, the classification performance became insignificant with the inclusion of HT patients (n=30). (p = 0.839 vs. p = 0.656). These results suggest that the coexistence of HT and DM may hinder the use of SD-PPG for noninvasive DM detection.KEYWORDS: Second-derivative photoplethysmographydiabetesnon-invasive cardiovascular screeningfingertip waveformshypertension Abbreviations Body Mass Index=BMIDiabetes Mellitus=DMDiabetes Mellitus Type-2=DM2Diastolic Blood Pressure=DBPHypertension=HTPhotoplethysmography=PPGSecond derivative of Photoplethysmography=SD-PPGVascular Ageing Index=VAISystolic Blood Pressure=SBPDisclosure statementNo potential conflict of interest was reported by the authors.Author contributionsStudy conception and design was made by Ahmet Tas. All authors (Ahmet Tas, Yaren Alan, Ilke Kara, Abdullah Savas, Muhammed Ikbal Bayhan, Diren Ekici, Zeynep Atay, Fatih Sezer, Cagla Kitapli, Sabahattin Umman, Murat Sezer) have contributed to material preparation, data collection and analysis (signal and/or statistical and/or intellectual) and interpretation of results. The first draft of the manuscript was written by Ahmet Tas, and all authors (Ahmet Tas, Yaren Alan, Ilke Kara, Abdullah Savas, Muhammed Ikbal Bayhan, Diren Ekici, Zeynep Atay, Fatih Sezer, Cagla Kitapli, Sabahattin Umman, Murat Sezer) commented on previous versions of the manuscript. All authors (Ahmet Tas, Yaren Alan, Ilke Kara, Abdullah Savas, Muhammed Ikbal Bayhan, Diren Ekici, Zeynep Atay, Fatih Sezer, Cagla Kitapli, Sabahattin Umman, Murat Sezer) read and approved the final manuscript.Ethics approvalThe data is retrieved from open-dataset published in Nature Scientific Data (https://www.nature.com/articles/sdata201820#). The data collection had ethical approval denoted in data descripting article and all participants have given written consent per open-data descripting article. All authors meet authorship criteria of ICMJE.Additional informationFundingThe authors reported there is no funding associated with the work featured in this article.
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来源期刊
CiteScore
2.80
自引率
6.20%
发文量
102
期刊介绍: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization is an international journal whose main goals are to promote solutions of excellence for both imaging and visualization of biomedical data, and establish links among researchers, clinicians, the medical technology sector and end-users. The journal provides a comprehensive forum for discussion of the current state-of-the-art in the scientific fields related to imaging and visualization, including, but not limited to: Applications of Imaging and Visualization Computational Bio- imaging and Visualization Computer Aided Diagnosis, Surgery, Therapy and Treatment Data Processing and Analysis Devices for Imaging and Visualization Grid and High Performance Computing for Imaging and Visualization Human Perception in Imaging and Visualization Image Processing and Analysis Image-based Geometric Modelling Imaging and Visualization in Biomechanics Imaging and Visualization in Biomedical Engineering Medical Clinics Medical Imaging and Visualization Multi-modal Imaging and Visualization Multiscale Imaging and Visualization Scientific Visualization Software Development for Imaging and Visualization Telemedicine Systems and Applications Virtual Reality Visual Data Mining and Knowledge Discovery.
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