Learning and non-learning algorithms for cuffless blood pressure measurement: a review

• Published in: Medical & biological engineering & computing Vol. 59; no. 6; pp. 1201 - 1222
• Main Authors: Agham, Nishigandha Dnyaneshwar, Chaskar, Uttam M.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021; Springer Nature B.V
• Subjects: Algorithms; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Blood pressure; Chronic illnesses; Computer Applications; Health care; Human Physiology; Imaging; Learning algorithms; Machine learning; Medical electronics; Medical equipment; Miniaturization; Monitoring systems; Position measurement; Prediction models; Pressure measurement; Radiology; Review Article; Telemedicine; Blood pressure; Learning algorithm; Non-learning algorithm; Machine learning
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-021-02362-6

The machine learning approach has gained a significant attention in the healthcare sector because of the prospect of developing new techniques for medical devices and handling the critical database of chronic diseases. The learning approach has potential to analyze complex medical data, disease diagnosis, and patient monitoring system, and to monitor e-health record. Non-invasive cuffless blood pressure (CLBP) measurement secured a significant position in the patient monitoring system. From a few recent decades, the importance of cuffless technology has been perceived towards continuous monitoring of blood pressure (BP) and supplementary efforts have been made towards its continuous monitoring. However, the optimal method that measures BP unambiguously and continuously has not yet emerged along with issues like calibration time, accuracy and long-term estimation of BP with miniaturizing hardware. The present study provides an insight into several learning algorithms along with their feature selection models. Various challenges and future improvements towards the current state of machine learning in healthcare industries are discussed in the present review. The bottom line of this study is to provide a comprehensive perspective of the machine learning approach of CLBP for the generation of highly precise predictive models for continuous BP measurement.