Association between Mean Heart Rate and Recurrence Quantification Analysis of Heart Rate Variability in EndStage Renal Disease Linear heart rate variability (HRV) indices are dependent on the mean heart rate, which has been demonstrated in different models (from sinoatrial cells to humans). The association between nonlinear HRV indices, including those provided by recurrence plot quantitative analysis (RQA), and the mean heart rate (or the mean cardiac period, also called meanNN) has been scarcely studied. For this purpose, we analyzed RQA indices of five minutelong HRV time series obtained in the supine position and during active standing from 30 healthy subjects and 29 endstage renal disease (ESRD) patients (before and after hemodialysis). In the supine position, ESRD patients showed shorter meanNN (i.e., faster heart rate) and decreased variability compared to healthy subjects. The healthy subjects responded to active standing by shortening the meanNN and decreasing HRV indices to reach similar values of ESRD patients. Bivariate correlations between all RQA indices and meanNN were significant in healthy subjects and ESRD after hemodialysis and for most RQA indices in ESRD patients before hemodialysis. Multiple linear regression analyses showed that RQA indices were also dependent on the position and the ESRD condition. Then, future studies should consider the association among RQA indices, meanNN, and these other factors for a correct interpretation of HRV.

Predicting intensive care outcomes in traumatic brain injury using heart rate variability measures with feature extraction strategies Prediction of patient outcome in medical intensive care units (ICU) may help for development of early interventional strategies. Several ICU scoring systems have been developed and are used to predict clinical deterioration of ICU patients. These scores are calculated from characteristics of patients and clinical records. Heart rate variability (HRV) is a correlate of cardiac autonomic regulation and has been evident as a marker in many critical diseases. It can be measured based on electrocardiogram (ECG) which is noninvasive and can be real time monitored. HRV has been identified as a promising xe2x80x98electronic biomarkerxe2x80x99 of disease severity. Traumatic brain injury (TBI) is a subset of critically ill patients, admitted to ICU. Changes of HRV for brain injured patients have been reported in several studies. This study aimed to utilize the continuous HRV collection from admission for the first 24 hours in the ICU in severe TBI patients, and develop a patient outcome prediction system. A feature extraction strategy was applied to measure the HRV fluctuation during time. A prediction model was developed based on HRV measures collected for the first day of patient admission to the ICU. The result was compared with current evaluated ones, and showed promising result for further development and potential for practical application.