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• W3003115507 abstract "Seizures are key characteristics epilepsy, which can be captured by scalp EEG. An accurate method of predicting seizures will help to alleviate the helplessness and uncertainty of patients. Seizures vary dramatically between patients and even in an individual patient. Here we propose to design a patient-specific seizure prediction model based on convolutional neural networks (CNNs). First, the short-time Fourier transform (STFT) is applied on 30-s EEG windows to extract spatiotemporal information of the preictal and interictal EEG signals. Then the CNN is built based on machine learning algorithms for automatic feature extraction and classification to achieve the prediction of seizures. The area under ROC Curve (AUC) is used to evaluate the performance of CNN models. The mean value of AUC is 0.94, the average false prediction rate (FPR) is 0.09/h, and the sensitivity of more than half of patients is above 95%." @default.
• W3003115507 created "2020-01-30" @default.
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• W3003115507 date "2019-10-01" @default.
• W3003115507 modified "2023-09-25" @default.
• W3003115507 title "Patient-Specific Seizure Prediction Using Convolutional Neural Networks and Scalp EEG" @default.
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• W3003115507 doi "https://doi.org/10.1109/cisp-bmei48845.2019.8965995" @default.
• W3003115507 hasPublicationYear "2019" @default.
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