Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

IF 17.2 1区工程技术 Q1 ENGINEERING, BIOMEDICAL

IEEE Reviews in Biomedical Engineering Pub Date : 2023-03-15 DOI:10.1109/RBME.2023.3271595

Lei Lu;Tingting Zhu;Davide Morelli;Andrew Creagh;Zhangdaihong Liu;Jenny Yang;Fenglin Liu;Yuan-Ting Zhang;David A. Clifton

{"title":"Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review","authors":"Lei Lu;Tingting Zhu;Davide Morelli;Andrew Creagh;Zhangdaihong Liu;Jenny Yang;Fenglin Liu;Yuan-Ting Zhang;David A. Clifton","doi":"10.1109/RBME.2023.3271595","DOIUrl":null,"url":null,"abstract":"Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"180-196"},"PeriodicalIF":17.2000,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10124275","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Reviews in Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10124275/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}

引用次数: 0

Abstract

Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.

查看原文本刊更多论文

心率变异性分析中的不确定性：系统回顾

心率变异性（HRV）是一项重要指标，在心血管疾病、糖尿病和心理健康等临床领域有多种应用。心率变异数据可从心电图和照相血压计信号中获取，然后利用信号过滤和数据分割等计算技术处理采样数据，计算心率变异指标。然而，数据采集、计算模型和生理因素带来的不确定性会导致信号质量下降，影响心率变异分析。因此，解决这些不确定性并开发先进的心率变异分析模型至关重要。虽然目前已有一些关于心率变异分析的综述，但它们主要集中在临床应用、心率变异方法的发展趋势或不确定性的特定方面，如测量噪声。本文全面回顾了心率变异分析中的不确定性，量化了其影响，并概述了潜在的解决方案。据我们所知，这是首次从工程师的角度对心率变异方法中的不确定性进行全面评述，并量化其对心率变异测量的影响。该综述对开发稳健可靠的模型至关重要，可作为该领域未来的重要参考资料，尤其是在处理心率变异分析中的不确定性时。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Reviews in Biomedical Engineering Engineering-Biomedical Engineering

CiteScore

31.70

自引率

0.60%

发文量

期刊介绍： IEEE Reviews in Biomedical Engineering (RBME) serves as a platform to review the state-of-the-art and trends in the interdisciplinary field of biomedical engineering, which encompasses engineering, life sciences, and medicine. The journal aims to consolidate research and reviews for members of all IEEE societies interested in biomedical engineering. Recognizing the demand for comprehensive reviews among authors of various IEEE journals, RBME addresses this need by receiving, reviewing, and publishing scholarly works under one umbrella. It covers a broad spectrum, from historical to modern developments in biomedical engineering and the integration of technologies from various IEEE societies into the life sciences and medicine.