Resumen
Heart rate variability (HRV) has emerged as an essential non-invasive tool for understanding cardiac autonomic function over the last few decades. This can be attributed to the direct connection between the heart?s rhythm and the activity of the sympathetic and parasympathetic nervous systems. The cost-effectiveness and ease with which one may obtain HRV data also make it an exciting and potential clinical tool for evaluating and identifying various health impairments. This article comprehensively describes a range of signal decomposition techniques and time-series modeling methods recently used in HRV analyses apart from the conventional HRV generation and feature extraction methods. Various weight-based feature selection approaches and dimensionality reduction techniques are summarized to assess the relevance of each HRV feature vector. The popular machine learning-based HRV feature classification techniques are also described. Some notable clinical applications of HRV analyses, like the detection of diabetes, sleep apnea, myocardial infarction, cardiac arrhythmia, hypertension, renal failure, psychiatric disorders, ANS Activity of Patients Undergoing Weaning from Mechanical Ventilation, and monitoring of fetal distress and neonatal critical care, are discussed. The latest research on the effect of external stimuli (like consuming alcohol) on autonomic nervous system (ANS) activity using HRV analyses is also summarized. The HRV analysis approaches summarized in our article can help future researchers to dive deep into their potential diagnostic applications.