title: New algorithm of mortality risk prediction for cardiovascular patients admitted in intensive care unit.
abstract: OBJECTIVE
  Recognizing and managing of admitted patients in intensive care unit (ICU) with high risk of mortality is important for maximizing the patient's outcomes and minimizing the costs. This study is based on linear and nonlinear analysis of heart rate variability (HRV) to design a classifier for mortality prediction of cardio vascular patients admitted to ICU.
  
  
  METHODS
  In this study we evaluated 90 cardiovascular ICU patients (45 males and 45 females). Linear and nonlinear features of HRV include SDNN, NN50, low frequency (LF), high frequency (HF), correlation dimension, approximate entropy; detrended fluctuation analysis (DFA) and Poincaré plot were analyzed. Paired sample t-test was used for statistical comparison. Finally, we fed these features to the Multi-Layer Perceptron (MLP) and Support Vector Machines (SVMs) to find a robust classification method to classify the patients with low risk and high risk of death.
  
  
  RESULTS
  Almost all HRV features measuring heart rate complexity were significantly decreased in the episode of half-hour before death. The results generated based on SVM and MLP classifiers show that SVM classifier is enable to distinguish high and low risk episodes with the total classification sensitivity, specificity, positive productivity and accuracy rate of 97.3%, 98.1%, 92.5% and 99.3%, respectively.
  
  
  CONCLUSIONS
  The results of the current study suggest that nonlinear features of the HRV signals could be show nonlinear dynamics.
authors: Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University Tehran, Iran.
authors: Mohammad Karimi
authors: MK
authors: Moridani
authors: Control and Intelligent Processing Centre of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran Tehran, Iran.
authors: Seyed Kamaledin
authors: SK
authors: Setarehdan
authors: Departments of Biomedical Engineering, Shahed University Tehran, Iran.
authors: Ali Motie
authors: AM
authors: Nasrabadi
authors: Loghman Medical Center, Shahid Beheshti University of Medical Sciences Tehran, Iran.
authors: Esmaeil
authors: E
authors: Hajinasrollah
keywords: HRV; ICU; MLP; Mortality prediction; SVM; linear and non-linear analysis