aceA jointly optimized hybrid framework for epileptic EEG artifact classification
Accurate classification of artifacts in long-term electroencephalography (EEG) recordings is essential for reliable automated epilepsy analysis, as artifacts may obscure epileptiform patterns and degrade downstream detection performance. However, existing methods still face challenges in complementary feature integration and coordinated optimization of feature selection and classifier parameters. To address the limited feature representation capability of handcrafted methods and the limited interpretability of d…
A jointly optimized hybrid classifier reached 99.30% accuracy on its constructed dataset and 98.15% on a separate recording-independent clinical test set, supporting more reliable automated epilepsy EEG analysis.
Evidence
- Peer-reviewedComputers in Biology and Medicine2026-10-16
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Truvace Impact Record TRV-2026-2141, v1: “A jointly optimized hybrid framework for epileptic EEG artifact classification.” Truvace, 2026-10-22. /record/TRV-2026-2141 (accessed at citation time). sha256 98f791d18bacd99e…
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