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Analysis of heart rate variability as a predictor of mortality in cardiovascular patients of intensive care unit

Moridani M. K., Setarehdan S. K., Nasrabadi A. M., Hajinasrollah E.

Biocybernetics and Biomedical Engineering

2015

Vol. 35, no. 4

217--226

Objective: Dynamic changes of heart rate variability (HRV) reflect autonomic dysfunction in cardiac disease. Some studies suggest the role of HRV in predicting intensive care unit (ICU) mortality. The main object of this study was analyzing the HRV to design an algorithm to predict mortality risk. Methods: We evaluated 80 cardiovascular ICU patients (45 males and 45 females), ranging from 45 to 70 years. Common time and frequency domain analysis, non-linear Poincaré plot and recurrence quantification analysis (RQA) were used to study the HRV in two episodes. The episodes include 8–4 h before death, and 4 h before death to death. Independent sample t-test was used as statistical analysis. Results: Statistical analysis indicates that frequency domain and Poincaré parameters such as LF/HF and SD2/SD1 show changes in transition to death episode (p < 0.05). Moreover, Lmean, vmax and RT measures showed meaningful changes (p < 0.01) in closer segments to the death. Conclusions: Analysis of physiological variables shows that there are significant differences in RQA measures in episodes close to death. These changes can be interpreted as more stability and determinism behavior of HRV in episodes close to death. RQA parameters can be used together with HRV parameters for description and prediction of mortality risk in ICU patients.

Automated detection of epileptic seizures using mixed-methodology: Wavelet-Chaos-KNN Classifier-Mutual Information

Mirzaei A., Ayatollahi A., Nasrabadi A. M.

Przegląd Elektrotechniczny

2011

R. 87, nr 4

220-223

Electroencephalogram (EEG) is the brain signal that contains the valuable information about different states of the brain. In this study EEG signals are analyzed for evaluating epileptic seizures in these signals and their sub-bands and comparing epileptic states with other states. A discrete wavelet transform is applied for decompose the EEGs into its sub-bands. The chaotic behavior of EEGs is evaluated by means ol normalized Shannon and spectral entropies. Entropy method is presented for detection of epileptic seizures through the analysis of EEGs and their sub-bands. At the end the mixture K-nearest neighbor and mutual information method is applied as a classifier to classify the different states in EEGs and their sub-bands. This method is applied to three different groups of EEG signals: 1) healthy states, 2) epileptic states during a seizure-free interval (interictal EEG), 3) epileptic states during a seizure (ictal EEG). The proposed method could classify different states with 99% accuracy.

Elektroencefalografia EEG jest analizą sygnału mózgu. W artykule przedstawiono metody analizy sygnału EEG stosowane w celu wykrycia epilepsji. Zastosowano dyskretną transformatę falkową do dekompozycji sygnału EEG. Wykorzystano metodę entropii do detekcji sygnału związanego z epilepsją. Metody zastosowano do trzech grup pacjentów: zdrowych, chorych na epilepsję i chorych w czasie ataku epilepsji.