Heart rate variability features for different stress classification.

AIM: Autonomic nervous system (ANS) activities during different types of stress could affect the electrocardiogram (ECG) signal. This study aimed to recognize the types of stress by using different ECG signals in order to prevent its actual physiological effects on the heart signal. METHOD: The ECG signal recorded by portable wrist bracelets from 20 students in during seven phases which incorporated three different types of stress and four relaxation phases. After different forms of windowing the signal, we used linear and non-linear features such as detrended fluctuation analysis (DFA), Poincaré plot, approximate and sample entropy, correlation dimension, and recurrence plot to extract various features of the heart rate variability (HRV). Then, different classifiers were used to identify the types of stress. RESULTS: The results showed a decrease in NN50, RMSSD, pNN50, and recurrence plot features, and an increase in the DFA method during stress stages, which show the effect of stress on heart rate. Also, by using the convolutional neural network (CNN), an average classification rate of 98 % was obtained in association with cognitive stress and that of 94.5 % in association with emotional stress. CONCLUSION: This paper showed that features extracted from HRV can detect the stress and non-stress stages with high significance. Also, the accuracy of this paper proved that the proposed method is successful in preventing the dangerous effects of different types of stress on the heart (Tab. 5, Fig. 6, Ref. 34). Text in PDF www.elis.sk Keywords: stress, heart rate variability, non-linear features, convolutional neural network, classification.

Heart rate variability as a biomarker for epilepsy seizure prediction.

OBJECTIVE: Epilepsy is a neurological disorder that causes seizures of many different types. Recent research has shown that epileptic seizures can be predicted by using the electrocardiogrami instead of the electroencephalogram. In this study, we used the heart rate variability that is generated by the fluctuating balance of sympathetic and parasympathetic nervous systems to predict epileptic seizures. METHODS: We studied 11 epilepsy patients to predict the seizure interval. With regar tos the fact that HRV signals are nonstationary, our analysis focused on linear features in the time and frequency domain of HRV signal such as RR Interval (RRI), mean heart rate (HR), high-frequency (HF) (0.15-0.40 Hz) and low-frequency (LF) (0.04-0.15 Hz), as well as LF/HF. Also, quantitative analyses of Poincaré plot features (SD1, SD2, and SD1/SD2 ratio) were performed. HRV signal was divided into intervals of 5 minutes. In each segment linear and nonlinear features were extracted and then the amount of each segment compared to the previous segment using a threshold. Finally, we evaluated the performance of our method using specificity and sensitivity. RESULTS: During seizures, mean HR, LF/HF, and SD2/SD1 ratio significantly increased while RRI significantly decreased. Significant differences between two groups were identified for several HRV features. Therefore, these parameters can be used as a useful feature to discriminate a seizure from a non-seizure The seizure prediction algorithm proposed based on HRV achieved 88.3% sensitivity and 86.2 % specificity. CONCLUSION: These results indicate that the HRV signal contains valuable information and can be a predictor for epilepsy seizure. Although our results in comparison with EEG ares a little bit weaker, the recording of ECG is much easier and faster than EEG. Also, our finding showed the results of this study are considerably better than recent research based on ECG (Tab. 1, Fig. 10, Ref. 17).

Non-linear Poincaré analysis of respiratory efforts in sleep apnea.

OBJECTIVES: Obstructive sleep apnea (OSA) is a risk factor for hypertension, has effects on cardiovascular system and increases the sympathetic activity. The aim of the study was to evaluate the effectiveness of the non-linear Poincaré plot analysis to predict OSA based on polysomnography (PSG). METHODS: The database of this study was collected by the sleep laboratory at the Philipps University in Marburg, Germany. It includes 24 PSG of men and women between 27-63 years old with obstructive and mixed sleep apnea. The start and end of apnea events in PSGs were marked. The Poincaré plots of pre-apneic phase including 4-1 minutes before apnea were evaluated. Wilcoxon test was used for statistical analysis. RESULTS: Poincaré analysis showed that the dynamics of chest and respiratory efforts changed two minutes before the apnea and SD1/SD2 ratios of these parameters significantly increased in the pre-apneic phase (p≤0.01). The SD1/SD2 ratio of nasal airflow did not show significant difference even in episodes close to apnea. CONCLUSIONS: Our results suggest that Poincaré plot parameters of PSG have the potential to be considered predictors of apnea with the ability to show the dynamic of changes, which could lead to pre-diagnosis or prediction of apnea about 2-3 minutes before its occurrence (Tab. 2, Fig. 4, Ref. 23).