Pre-ictal heart rate variability assessment of epileptic seizures by means of linear and non-linear analyses
ABSTRACTObjective: The purpose of the present study was to analyze the effects of epilepsy on the autonomic control of the heart in pre-ictal phase in order to find an algorithm of early detection of seizure onset. Methods: Overall 133 epileptic seizures were analyzed from 12 patients with epilepsy (seven males and five females; mean age 43.91 years, SD: 10.16) participated in this study. Single lead electrocardiogram recordings of epileptic patients were compiled. 240, 90-30, 30-10 and 5 minutes heart rate variability (HRV) signals of preseizure were chosen for analysis of heart rate. As HRV signals are non-stationary, a set of time and frequency domain features (Mean HR, Triangular Index, LF, HF, LF/HF) and nonlinear parameters (SD1, SD2 and SD2/SD1 indices derived from Poincaré plots) extracted from HRV is analyzed. Statistical analysis was performed using paired sample t-test for comparisons of the segments and differences between pre-ictal segments were evaluated by Tukey tests. Results: There was slight tachycardia in segments near the seizure (30 minutes before: 85.3517 bpm, 5 minutes before: 119.3630.82 bpm, p=0.0207) which significantly differ from baseline in segments far from seizure (240 minutes before: 66.5211.7 bpm). Also there was significant increase in LF/ HF ratio (30 minutes before: 1.10.22, 5 minutes before: 2.120.5, p=0.0332) and SD2/SD1 ratio (30 minutes before: 1.20.15, 5 minutes before: 2.030.55, p=0.0431) when compared to segments far from the seizure (240 minutes before: 0.780.24 and 0.780.14) respectively. Although there was about decrease of triangular index in segments near the seizure the percentage of decrease was not comparable to segments far from the seizure. Conclusion: Significant changes of HRV parameters in pre-ictal (5 minutes before the seizure) are obviously higher in comparison to interictal baseline. Pre-ictal significant changes of HRV suggesting that this time can be considered as prediction time for designing an algorithm of early detection of seizure onset based on HRV. (
In this paper we used two new features i.e. T-wave integral and total integral as extracted feature from one cycle of normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our previous work we used some features of body surface potential map data for this aim. But we know the standard ECG is more popular, so we focused our detection and localization of MI on standard ECG. We use the T-wave integral because this feature is important impression of T-wave in MI. The second feature in this research is total integral of one ECG cycle, because we believe that the MI affects the morphology of the ECG signal which leads to total integral changes. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI, because this method has very good accuracy for classification of normal signal and abnormal signal. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 76% for accuracy in test data for localization and over 94% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve the accuracy of classification by adding more features in this method. A simple method based on using only two features which were extracted from standard ECG is presented and has good accuracy in MI localization.
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