Khan Mamun Mohammad Mahbubur Rahman, Sherif Ahmed
Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA.
School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS 39406, USA.
Bioengineering (Basel). 2022 Dec 24;10(1):27. doi: 10.3390/bioengineering10010027.
Hypertension is a chronic condition that is one of the prominent reasons behind cardiovascular disease, brain stroke, and organ failure. Left unnoticed and untreated, the deterioration in a health condition could even result in mortality. If it can be detected early, with proper treatment, undesirable outcomes can be avoided. Until now, the gold standard is the invasive way of measuring blood pressure (BP) using a catheter. Additionally, the cuff-based and noninvasive methods are too cumbersome or inconvenient for frequent measurement of BP. With the advancement of sensor technology, signal processing techniques, and machine learning algorithms, researchers are trying to find the perfect relationships between biomedical signals and changes in BP. This paper is a literature review of the studies conducted on the cuffless noninvasive measurement of BP using biomedical signals. Relevant articles were selected using specific criteria, then traditional techniques for BP measurement were discussed along with a motivation for cuffless measurement use of biomedical signals and machine learning algorithms. The review focused on the progression of different noninvasive cuffless techniques rather than comparing performance among different studies. The literature survey concluded that the use of deep learning proved to be the most accurate among all the cuffless measurement techniques. On the other side, this accuracy has several disadvantages, such as lack of interpretability, computationally extensive, standard validation protocol, and lack of collaboration with health professionals. Additionally, the continuing work by researchers is progressing with a potential solution for these challenges. Finally, future research directions have been provided to encounter the challenges.
高血压是一种慢性疾病，是心血管疾病、脑卒中和器官衰竭的主要原因之一。如果不被注意和治疗，健康状况的恶化甚至可能导致死亡。如果能早期发现并进行适当治疗，就可以避免不良后果。到目前为止，金标准是使用导管侵入性测量血压（BP）的方法。此外，基于袖带的无创方法对于频繁测量血压来说过于繁琐或不便。随着传感器技术、信号处理技术和机器学习算法的进步，研究人员正在努力寻找生物医学信号与血压变化之间的完美关系。本文是一篇关于使用生物医学信号进行无袖带无创血压测量研究的文献综述。使用特定标准选择相关文章，然后讨论传统的血压测量技术，以及使用生物医学信号和机器学习算法进行无袖带测量的动机。该综述侧重于不同无创无袖带技术的进展，而不是比较不同研究之间的性能。文献调查得出结论，在所有无袖带测量技术中，深度学习的使用被证明是最准确的。另一方面，这种准确性有几个缺点，如缺乏可解释性、计算量大、标准验证协议以及缺乏与卫生专业人员的合作。此外，研究人员正在继续努力，为这些挑战寻找潜在的解决方案。最后，提供了未来的研究方向以应对这些挑战。
