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.

Simultaneous Multiple Features Tracking of Beats: A Representation Learning Approach to Reduce False Alarm Rate in ICUs The high rate of false alarms is a key challenge related to patient care in intensive care units (ICUs) that can result in delayed responses of the medical staff. Several rulebased and machine learningbased techniques have been developed to address this problem. However, the majority of these methods rely on the availability of different physiological signals such as different electrocardiogram (ECG) leads, arterial blood pressure (ABP), and photoplethysmogram (PPG), where each signal is analyzed by an independent processing unit and the results are fed to an algorithm to determine an alarm. That calls for novel methods that can accurately detect the cardiac events by only accessing one signal (e.g., ECG) with a low level of computation and sensors requirement. We propose a novel and robust representation learning framework for ECG analysis that only rely on a single lead ECG signal and yet achieves considerably better performance compared to the stateoftheart works in this domain, without relying on an expert knowledge. We evaluate the performance of this method using the xe2x80x9c2015 Physionet computing in cardiology challengexe2x80x9d dataset. To the best of our knowledge, the best previously reported performance is based on both expert knowledge and machine learning where all available signals of ECG, ABP and PPG are utilized. Our proposed method reaches the performance of 97.3%, 95.5%, and 90.8% in terms of sensitivity, specificity, and the challengeu0027s score, respectively for the detection of five arrhythmias when only one single ECG lead signals is used without any expert knowledge11This material is based upon work supported by the National Science Foundation under Grant Number 1657260. Research reported in this publication was also supported by the National Institute On Minority Health and Health Disparities of the National Institutes of Health under Award Number U54MD012388. 22The copyright notice: 97817281186731931.00 xc2xa92019 IEEE.