Unsupervised Feature Representation Based on Deep Boltzmann Machine for Seizure Detection
The Electroencephalogram (EEG) pattern of seizure activities is highly individual-dependent and requires experienced specialists to annotate seizure events. It is clinically time-consuming and error-prone to identify seizure activities by visually scanning EEG signals. Since EEG data are heavily und...
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| Asıl Yazarlar: | , , , |
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| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
IEEE
2023-01-01
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| Seri Bilgileri: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Konular: | |
| Online Erişim: | https://ieeexplore.ieee.org/document/10064189/ |
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