K-fold cross-validation table.

<div><p>Epilepsy affects around 50 million people globally, causing significant burdens. While many methods predict seizures, current models struggle with handling spatiotemporal features and balancing accuracy with computational efficiency.This paper introduces a novel deep learning arc...

وصف كامل

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Dianli Wang (22765892) (author)
مؤلفون آخرون:	Enping Li (17301730) (author), Yang Wang (5921) (author), Zhiyang Liu (4332328) (author), Aixia Sun (5269634) (author), Wei Wei (21173) (author), Xuning Zhang (6934130) (author), Cheng Peng (118834) (author), Fengtao Wei (22765895) (author)
منشور في:	2025
الموضوعات:	Biochemistry Neuroscience Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified scale convolutional network predict epileptic seizures handling spatiotemporal features gated recurrent units extract spatiotemporal features effective new tool current models struggle causing significant burdens 89 %, sensitivity 52 %, specificity processing medical signals cgtnet incorporating spatio ethz eeg datasets 53 %, eeg signals technical innovations specifically designed sparse transformers seizure prediction rigorous evaluation research provides renowned chb paper introduces model achieved mcc value innovative approach immense potential grus ), exceptional performance early detection deep learning continuous monitoring computational efficiency cgtnet enhances auroc value analyzing elec also validate advancing healthcare
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

K-fold cross-validation table.

مواد مشابهة