Real Time Risk Prediction of heart patients using HRV and IoT: A Survey

Abstract

In today's world, the leading cause of death is heart disease. The term heart disease is described as several different conditions that affect the heart. There are many types of heart disease, but the most common are coronary heart disease, congestive heart failure, and arrhythmia. Currently, the healthcare system is hospital essential which is inefficient to treatthese conditions, which require immediate assistance. This is a roundabout way to point to a rise in the death rates. Heart rate variability (HRV) is a useful tool for identifying and monitoring potential risk factors for heart disease. HRV measures the time between each heartbeat and can be used to identify subtle changes in heart rate that may be indicative of an underlying health condition. With the help of Machine learning and (Internet of Things) IoT-based heart patients real-time monitoring and predicting risk analysis will enable doctors to view patient's health status online. Internet of Things (IoT) technology to continuously track and monitor vital signs,including heart rate, pulse rate, and body temperature. In addition to these traditional vital signs, the system also includes afeature for monitoring heart rate variability (HRV), which has been shown to be a useful indicator of heart disease risk prediction. Using machine learning and HRV capabilities, the system will be able to assess health status and anticipate patient'sheart health (low-risk or high-risk) based on the ECG waveform and HRV characteristics such as time domain and frequency domain values