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Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or neuro‐physiologists; a process that is considered to have a comparatively low inter‐rater agreement. Furthermore, the new data interpretation consumes an excessive amount of time and resources. Hence, an automatic seizure detection...
This paper presents patient-specific epileptic seizure detection approach based on Common Spatial Pattern (CSP) and its variants; Diagonal Loading Common Spatial Pattern (DLCSP), and Tikhonov Regularization Common Spatial Pattern (TRCSP). In this proposed approach, multi-channel scalp Electroencephalogram (sEEG) signals are traced and segmented into overlapping segments for both normal and epileptic...
This paper proposes a novel patient-specific approach to channel selection and seizure detection based on estimating the histograms of multi-channel scalp electroencephalography (sEEG) signals. It consists of two main phases: training and testing. In the training phase, the signal is segmented into non-overlapping 10-second segments, with five histograms estimated for each segment. These histograms...
Epilepsy is a brain disorder, which affects around 1% of world population. The life of epilepsy patients can be improved by predicting seizures before its occurrence. It has been observed that EEG signals during the pre-seizure state are less chaotic compared to their behavior at normal state. Therefore, chaoticity measure can be used to develop seizure predictor. In this paper, we propose seizure...
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