ArticleCognitive neurodynamics2023
Generalizable epileptic seizures prediction based on deep transfer learning.
Article in Cognitive neurodynamics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- [Research progress in electroencephalogram-based brain age prediction].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Privacy-preserving multi-source semi-supervised domain adaptation for seizure prediction.Cognitive neurodynamics · 2024Article
- Intertumoral and intratumoral barriers as approaches for drug delivery and theranostics to solid tumors using stimuli-responsive materials.Mikrochimica acta · 2024Review
- Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
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Abstract
Predicting seizures before they happen can help prevent them through medication. In this research, first, a total of 22 features were extracted from 5-s segmented EEG signals. Second, tensors were developed as inputs for different deep transfer learning models to find the best model for predicting epileptic seizures. The effect of Pre-ictal state duration was also investigated by selecting four different intervals of 10, 20, 30, and 40 min. Then, nine models were created by combining three ImageNet convolutional networks with three classifiers and were examined for predicting seizures patient-dependently. The Xception convolutional network with a Fully Connected (FC) classifier achieved an average sensitivity of 98.47% and a False Prediction Rate (FPR) of 0.031 h
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