Prediction of vascular events in heart disease patients with HRV analysis using fragmented autocorrelation

Heart disease is one of the leading causes of death worldwide. To treat patients with heart disease, continuous monitoring of their condition is a crucial step. However, this treatment process often has significant costs. In several previous studies, Heart Rate Variability (HRV) in the form of time series has been used to analyze patients' heart conditions as a health indicator. One method that can be used to analyze such time series data is the Fragmented Autocorrelation Function (FACF), which has been proven effective in classifying time series data with good results. This study aims to apply the FACF method in HRV analysis for monitoring heart disease patients, with the hope of providing a more efficient and accurate solution for heart patient care at a lower cost.