基于人工智能的听诊波形和示波波形血压估计方法综述

IF 17.2 1区 工程技术 Q1 ENGINEERING, BIOMEDICAL
Ahmadreza Argha;Branko G. Celler;Nigel H. Lovell
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引用次数: 9

摘要

心血管疾病被认为是全球头号死亡原因,血压升高是最大的单一风险因素。因此,血压是一个重要的生理参数,用作心血管健康的指标。自动无创血压(NIBP)测量设备的使用越来越多,因为它们可以在没有专业知识的情况下使用,而且血压测量可以由患者在家进行。基于无创袖带的监测是测量血压的主要方法。虽然示波测量技术是最常见的,但已经在听诊技术的基础上开发了一些自动NIBP测量方法。通过利用专家注释的(相对)大的BP数据,可以使用机器学习和统计概念来训练模型,以开发新的NIBP估计算法。在人工智能(AI)技术中,深度学习由于其在数据分类和特征提取问题上的优势,在不同领域受到了越来越多的关注。本文综述了基于人工智能的BP估计方法,重点介绍了该领域基于深度学习的方法的最新进展。讨论了今天提出的各种体系结构和方法,以澄清它们的优势和劣势。根据文献综述,深度学习为BP估计领域带来了看似合理的好处。我们还讨论了一些可能阻碍深度学习在该领域广泛采用的局限性,并提出了克服这些挑战的框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A Methodological Review
Cardiovascular disease is known as the number one cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as they can be used without expertise and BP measurement can be performed by patients at home. Non-invasive cuff-based monitoring is the dominant method for BP measurement. While the oscillometric technique is most common, some automated NIBP measurement methods have been developed based on the auscultatory technique. By utilizing (relatively) large BP data annotated by experts, models can be trained using machine learning and statistical concepts to develop novel NIBP estimation algorithms. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification and feature extraction problems. This paper reviews AI-based BP estimation methods with a focus on recent advances in deep learning-based approaches within the field. Various architectures and methodologies proposed todate are discussed to clarify their strengths and weaknesses. Based on the literature reviewed, deep learning brings plausible benefits to the field of BP estimation. We also discuss some limitations which can hinder the widespread adoption of deep learning in the field and suggest frameworks to overcome these challenges.
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来源期刊
IEEE Reviews in Biomedical Engineering
IEEE Reviews in Biomedical Engineering Engineering-Biomedical Engineering
CiteScore
31.70
自引率
0.60%
发文量
93
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.
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