IoMT-based Heart Rate Variability Analysis with Passive FBG Sensors for Improved Health Monitoring

Abstract

The use of smart healthcare systems to monitor cardiac parameters has gained widespread popularity globally due to advancements in technology. The Internet of Medical Things (IoMT) has become an integral part of modern healthcare by facilitating the efficient monitoring of vital signs through advanced sensors. Heart Rate variability (HRV) characteristics, which provide valuable insights into a patient’s health, have become indispensable in healthcare applications. Fiber Bragg Grating (FBG) based optical sensors have emerged as cutting-edge technology for continuous monitoring of several cardiac parameters among the new alternatives. Recent technical advances have improved the accuracy of these sensors, allowing for the early identification and prognosis of heart illnesses, potentially saving lives. This article delves into the design, construction, and structural analysis of a passive optical FBG sensor capable of real-time acquisition of HRV parameters such as standard Deviation of Normal-to-Normal (SDNN), Root Mean Square of Successive Differences (RMSSD), and Percentage of Successive Normal-to-Normal intervals (PNN50) differing by more than 50 ms, along with Heart Rate (HR). It also provides enhanced signal processing methods as well as an IoT-based architectural architecture. Experimental research in a laboratory including five participants (three males and two females) revealed good performance, with an error rate of less than 10 percent when compared to a typical HR monitor. This intelligent technology detects arrhythmia, coronary heart disease, aortic disorders, and strokes with high accuracy, providing a substantial contribution to healthcare. The combination of Fiber Bragg Grating (FBG) sensors, Internet of Things (IoT) architecture, and cutting-edge technology has enormous potential for improving cardiac monitoring and patient outcomes.