Septic Shock Prediction by Real Time Monitoring of Heart Rate Variability

Abstract

We propose a method for septic shock prediction by real time monitoring of heart rate variability (HRV) for patients in intensive care unit (ICU). An objective criterion that is related to septic occurrence and can be measured automatically in real time is required to detect septic occurrence. Although HRV is a candidate of such criteria, an automatic estimation of HRV has been difficult because there are many arrhythmias in ICU patients. In this study, we employed a stochastic model on R-R interval series, namely heartbeat interval series, in order to detect abnormal heartbeats in normal R-R interval series automatically. Abnormal heartbeats and very low frequency components are eliminated from R-R series by using the stochastic model. Applying the elimination process before HRV estimation, we identified a distinctive V-shaped temporal pattern in HRV series before septic shock was occurred. The V-shaped pattern is applicable as a signal of septic occurrence. We finally propose a real-time monitoring for sepsis occurrence by using the V-shaped pattern in HRV.