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) parameters, which provide valuable insights into a patient's condition, are now a crucial aspect of healthcare applications. Among the innovative solutions available, fiber Bragg grating (FBG) based optical sensors have emerged as a promising technology for continuous monitoring of various cardiac parameters. Recent technological breakthroughs have made these sensors highly accurate, enabling early detection and prediction of cardiac diseases, significantly impacting lives. This article focuses on the design, construction, and structural analysis of a passive optical FBG sensor capable of acquiring real-time HRV parameters such as heart rate, standard deviation of normal-to-normal intervals, root mean square of successive differences, and percentage of successive NN intervals differing by more than 50 ms. Additionally, advanced signal processing algorithms and an IoT-based architectural design are presented. An experimental study conducted in a laboratory involved five subjects, three males and two females, and demonstrated satisfactory performance with an error rate of less than 10% compared to a standard HR monitor. This intelligent system can detect arrhythmia, coronary heart disease, aortic diseases, and strokes, thereby making a significant contribution to healthcare. The combination of FBG sensors, IoT architecture, and advanced technology holds immense potential for enhancing cardiac monitoring and improving patient outcomes.