Automatic and Quantitative Electroencephalographic Characterization of Drug-Resistant Epilepsy in Neonatal KCNQ2 Epileptic Encephalopathy

KCNQ2 epileptic encephalopathy is relatively common in early-onset neonatal epileptic encephalopathy and seizure severity varied widely, categorized as drug-sensitive epilepsy and drug-resistant epilepsy. However, in clinical practice, anti-seizure medicines need to be gradually adjusted based on se...

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Main Authors: Zheng Zeng (Author), Yan Xu (Author), Chen Chen (Author), Ligang Zhou (Author), Yalin Wang (Author), Minghui Liu (Author), Long Meng (Author), Yuanfeng Zhou (Author), Wei Chen (Author)
Format: Book
Published: IEEE, 2023-01-01T00:00:00Z.
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042 |a dc 
100 1 0 |a Zheng Zeng  |e author 
700 1 0 |a Yan Xu  |e author 
700 1 0 |a Chen Chen  |e author 
700 1 0 |a Ligang Zhou  |e author 
700 1 0 |a Yalin Wang  |e author 
700 1 0 |a Minghui Liu  |e author 
700 1 0 |a Long Meng  |e author 
700 1 0 |a Yuanfeng Zhou  |e author 
700 1 0 |a Wei Chen  |e author 
245 0 0 |a Automatic and Quantitative Electroencephalographic Characterization of Drug-Resistant Epilepsy in Neonatal KCNQ2 Epileptic Encephalopathy 
260 |b IEEE,   |c 2023-01-01T00:00:00Z. 
500 |a 1558-0210 
500 |a 10.1109/TNSRE.2023.3294909 
520 |a KCNQ2 epileptic encephalopathy is relatively common in early-onset neonatal epileptic encephalopathy and seizure severity varied widely, categorized as drug-sensitive epilepsy and drug-resistant epilepsy. However, in clinical practice, anti-seizure medicines need to be gradually adjusted based on seizure control which undoubtedly increases the economic burden of patients, so further positive anti-seizure regimens depend on whether seizure severity can be predicted in advance. In this paper, we proposed a reliable assessment to differentiate between drug-sensitive epilepsy and drug-resistant epilepsy caused by KCNQ2 pathogenic variants. Based on the electroencephalogram (EEG) and electrooculogram (EOG) signals, twenty-four classical temporal and spectral domain features were extracted and Gradient Boosting Decision Tree (GBDT) was employed to distinguish between patients with drug-sensitive epilepsy and drug-resistant epilepsy. In addition, we also systematically investigated the impact of channel combination and feature combination based on the forward stepwise selection strategy. By employing selected channels and features, the classification accuracy can reach 81.25% with a sensitivity of 57.14% and specificity of 100%. Compared with the state-of-the-art techniques, including the functional network, effective network, and common spatial patterns, the improvement of accuracy ranges from 37.5% to 56.25%, indicating the superiority of our proposed method. Overall, the proposed method may provide a promising tool to distinguish different seizure outcomes of KCNQ2 epileptic encephalopathy. 
546 |a EN 
690 |a KCNQ2 epileptic encephalopathy 
690 |a drug-resistant epilepsy 
690 |a electroencephalogram (EEG) 
690 |a gradient boosting decision tree (GBDT) 
690 |a Medical technology 
690 |a R855-855.5 
690 |a Therapeutics. Pharmacology 
690 |a RM1-950 
655 7 |a article  |2 local 
786 0 |n IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 3004-3014 (2023) 
787 0 |n https://ieeexplore.ieee.org/document/10182364/ 
787 0 |n https://doaj.org/toc/1558-0210 
856 4 1 |u https://doaj.org/article/d9fb8a0ffcad42c084c50236e9a3da94  |z Connect to this object online.