Heart Rate Variation Analysis for Predicting Heart Conditions Using PPG and ECG Data: A Review

Abstract

An advanced optical methodology known as photoplethysmography(PPG) is employed to assess fluctuations in blood-volume within dermal tissues. Heart rate monitoring is commonly integrated into wearable devices. PPG’s popularity derives from its cost effectiveness and convenience where continuous monitoring on the move is essential, such as in a disaster situation. PPG is prone to motion artifacts, though, which might skew the signals that are gathered. However, in comparison, the information that Electrocardiography (ECG) provides is the gold standard in tracking heart electrical activity — highly precise data on cardiac rhythm. ECG has a wide scope of applications in the clinical environment for the acquisition of heart rate and abnormality detection, to further elucidate details of cardiac performance. Autonomic control of the heart is a crucial indicator that is taken from PPG and ECG data and is used to show the Heart Rate Variability (HRV) or Phase I Workload status. The condition is fundamental in the diagnosis of heart problems, including hypertension, arrhythmias and cardiac diseases. This has led to the growing importance of analysis of HRV for early identification and prognosis of heart related health related issues. This review provides and compares HRV analysis using PPG and ECG for their predictive ability in the assessment of heart conditions. It then examines the properties of HRV data from both modalities informed by Signal Processing (SP) techniques to improve reliability. It reviews their contributions to improving the monitoring of heart conditions and to the more general landscape of HRV analysis in health care.