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Electroencephalography (EEG) is essential for tracking brain activity and identifying seizure effects. However, epileptic behaviour can only be detected after a specialist has carefully analysed all EEG recordings along with a proper history of the patient. A skilled physician is required for the right epilepsy diagnosis and therapy. But most of th...
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Context 1
... CHB-MIT and TUHEEG. Table 3 describes the performance of CNN model in the FH database. Total 13 patients(PAT) results are noted and the average of all the patients for three binary classes i.e. preictal versus interictal, ictal versus interictal and postictal versus interictal are 79.7%, 93.69%, and 83.85% respectively. ...Context 2
... patient numbers 1, 3,6,15,18,21 shows 100% accuracy for all the three binary classification method. Table 3, Table 4 and Table 5 shows part wise accuracy for FH dataset and CHB-MIT dataset and two class classification accuracy for TUHEEG dataset respectively. Table 4 describes the performance of CNN model in the CHB-MIT database. ...Context 3
... this work number of seizure events considered from FH and CHB-MIT are 59 and 64 respectively. From each patient mentioned in Table 3 and Table 4, preictal, postictal, and interictal EEG signals are extracted. From TUHEEG dataset, 51 events considered for preictal, ictal and postictal and 44 events are considered for interictal stage. ...Similar publications
p> Seizure prediction of epileptic preictal period through electroencephalogram (EEG) signals is important for clinical epilepsy diagnosis. However, recent deep learning-based methods commonly employ intra-subject training strategy and need sufficient data, which are laborious and time-consuming for a practical system and pose a great challenge for...
