دورية أكاديمية
Epilepsy seizure prediction with few-shot learning method
| العنوان: | Epilepsy seizure prediction with few-shot learning method |
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| المؤلفون: | Jamal Nazari, Ali Motie Nasrabadi, Mohammad Bagher Menhaj, Somayeh Raiesdana |
| المصدر: | Brain Informatics, Vol 9, Iss 1, Pp 1-9 (2022) |
| بيانات النشر: | SpringerOpen, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Computer software |
| مصطلحات موضوعية: | Seizure prediction, Epilepsy, EEG, CNN, Few-shot learning, Computer applications to medicine. Medical informatics, R858-859.7, Computer software, QA76.75-76.765 |
| الوصف: | Abstract Epileptic seizures prediction and timely alarms allow the patient to take effective and preventive actions. In this paper, a convolutional neural network (CNN) is proposed to diagnose the preictal period. Our goal is for those epileptic patients in whom seizures occur late and it is very challenging to record the preictal signal for them. In the previous works, generalized methods were inevitably used for this group of patients which were not very accurate. Our approach to solve this problem is to provide a few-shot learning method. This method, having the previous knowledge, is trained with only a small number of samples, learns new tasks and reduces the efforts to collect more data. Evaluation results for three patients from the CHB–MIT database, for a 10-min seizure prediction horizon (SPH) and a 20-min seizure occurrence period (SOP), averaged sensitivity of 95.70% and a false prediction rate (FPR) of 0.057/h and for the 5-min prediction horizon and the 25-min seizure occurrence period averaged sensitivity of 98.52% and a false prediction rate of (FPR) of 0.045/h. The proposed few-shot learning method, based on previous knowledge gained from the generalizable method, is regulated with a few new patient samples for the patient. Our results show that the accuracy obtained in this method is higher than the generalizable methods. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2198-4018 2198-4026 |
| Relation: | https://doaj.org/toc/2198-4018; https://doaj.org/toc/2198-4026 |
| DOI: | 10.1186/s40708-022-00170-8 |
| URL الوصول: | https://doaj.org/article/a1614e5ba1294809af5c70ba7a29de55 |
| رقم الأكسشن: | edsdoj.1614e5ba1294809af5c70ba7a29de55 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 21984018 21984026 |
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| DOI: | 10.1186/s40708-022-00170-8 |