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- W2892654155 abstract "Electroencephalogram (EEG) is commonly used for analyzing numerous psychological states of the brain. However, epileptic seizure prediction from EEG signals is quite challenging since it has more fluctuating information about the behaviour of the brain. Analyzing such long-term EEG signals to discriminate between interictal versus ictal regions is a difficult task. Also, EEG signals can be affected by noises from different sources. The proposed work presents an efficient approach based on Weighted Visibility Graph (WVG) for seizure prediction. In this work, the EEG signals are filtered to remove the artifacts due to power supply noise and then the filtered EEG time series data is segmented. The segmented time series data is converted into a complex network called WVG. This WVG inherits the dynamic characteristics of the EEG signal from which it is created. Features like mean degree, mean weighted degree and mean entropy are extracted from the WVG. These features are used to derive the essential characteristics of EEG from the WVG. Finally, classification is done using Support Vector Machine (SVM). The experiments show that the proposed system provides better performance than the existing methods in prediction of seizure in ictal as well as interictal states of EEG over the benchmark dataset." @default.
- W2892654155 created "2018-10-05" @default.
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- W2892654155 date "2018-01-01" @default.
- W2892654155 modified "2023-09-23" @default.
- W2892654155 title "Epileptic Seizure Prediction Using Weighted Visibility Graph" @default.
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- W2892654155 doi "https://doi.org/10.1007/978-981-13-1936-5_48" @default.
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