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- W4298152511 abstract "Epilepsy is a neurological disorder that affects a large number of people. When epilepsy first manifests, electroencephalogram (EEG) signals may be used to monitor the patient's status, and early diagnosis and treatment might potentially save a patient's life. The key to developing successful epilepsy detection and identification studies is to develop effective features and classifiers. To enhance epilepsy identification, we propose a multiview epilepsy detection approach that blends deep and surface characteristics. The algorithm first extracts shallow features from the EEG signal's frequency and time-frequency domains using FFT and WPD, then learns deep features from the EEG signal's frequency and time-frequency domains using a convolutional neural network (CNN), and finally detects shallow and deep features from the EEG signal using the multiview TSK fuzzy system. Classification models are created using characteristics. According to our experimental study, the proposed shallow and deep features have a greater influence on epilepsy diagnosis in EEG data than frequently used feature extraction approaches such as PCA and linear discriminant analysis (LDA). Additionally, the classification impact of the perspective epilepsy detection algorithm is more than 1% greater than that of the single-view algorithm and more than 5% greater than the single-view method's average detection effect." @default.
- W4298152511 created "2022-10-01" @default.
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- W4298152511 date "2023-01-01" @default.
- W4298152511 modified "2023-09-27" @default.
- W4298152511 title "Recurrent neural network model for identifying epilepsy based neurological auditory disorder" @default.
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- W4298152511 doi "https://doi.org/10.1016/b978-0-323-90277-9.00023-7" @default.
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