心力衰竭(HF)的全球患病率不断增长,因此需要创新的方法来进行心肌功能障碍的早期诊断和分类。近几十年来,非侵入性的基于传感器的技术有显著先进的心脏护理。这些技术简化了研究,有助于早期发现,确认血液动力学参数,并支持临床决策以评估心肌性能。本讨论探讨了经过验证的增强功能,挑战,以及心力衰竭和功能障碍建模的未来趋势,所有接地在使用非侵入式传感技术。这种方法的综合解决了现实世界的复杂性,并预测了心脏评估中的变革性变化。在五个数据库中进行了全面搜索,包括PubMed,WebofScience,Scopus,IEEEXplore,和谷歌学者,查找2009年至2023年3月之间发表的文章。目的是确定在对其拟议方法进行质量评估方面表现卓越的研究项目,通过基于比较标准的评级方法来实现。目的是查明将这些项目与具有可比目标的其他项目区分开来的独特特征。为诊断确定的技术,分类,和心力衰竭的表征,收缩和舒张功能障碍包括两个主要类别。第一个涉及与患者的间接互动,例如心冲击图(BCG),心阻抗图(ICG),光电体积描记术(PPG),和心电图(ECG)。这些方法翻译或传达心肌活动的影响。第二类包括非接触式传感设置,如基于成像工具的心脏模拟器,心肌表现的表现通过介质传播。当代基于非侵入性传感器的方法主要是为家庭量身定制的,远程,和连续监测心肌性能。这些技术利用机器学习方法,证明令人鼓舞的结果。算法的评估集中在如何选择临床终点,在评估这些方法的有效性方面显示出有希望的进展。
The growing global prevalence of heart failure (HF) necessitates innovative methods for early diagnosis and classification of myocardial dysfunction. In recent decades, non-invasive sensor-based technologies have significantly advanced cardiac care. These technologies ease research, aid in early detection, confirm hemodynamic parameters, and support clinical decision-making for assessing myocardial performance. This discussion explores validated enhancements, challenges, and future trends in heart failure and dysfunction modeling, all grounded in the use of non-invasive sensing technologies. This synthesis of methodologies addresses real-world complexities and predicts transformative shifts in cardiac assessment. A comprehensive search was performed across five databases, including PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar, to find articles published between 2009 and March 2023. The aim was to identify research projects displaying excellence in quality assessment of their proposed methodologies, achieved through a comparative criteria-based rating approach. The intention was to pinpoint distinctive features that differentiate these projects from others with comparable objectives. The techniques identified for the diagnosis, classification, and characterization of heart failure, systolic and diastolic dysfunction encompass two primary categories. The first involves indirect interaction with the patient, such as ballistocardiogram (BCG), impedance cardiography (ICG), photoplethysmography (PPG), and electrocardiogram (ECG). These methods translate or convey the effects of myocardial activity. The second category comprises non-contact sensing setups like cardiac simulators based on imaging tools, where the manifestations of myocardial performance propagate through a medium. Contemporary non-invasive sensor-based methodologies are primarily tailored for home, remote, and continuous monitoring of myocardial performance. These techniques leverage machine learning approaches, proving encouraging outcomes. Evaluation of algorithms is centered on how clinical endpoints are selected, showing promising progress in assessing these approaches\' efficacy.