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.

Keywords: HRV; ICU; MLP; Mortality prediction; SVM; linear and non-linear analysis.

Figure 1

The block diagram of the proposed classification algorithm.

Figure 2

A simplified explanation of false nearest neighbors.

Figure 3

A standard Poincaré plot (τ=1) of RR intervals of a healthy subject.

Figure 4

Multi-layer perceptron neural network structure.