Jia-Hui Zhang1, Xiong Han1, Hong-Wei Zhao2, Di Zhao3, Na Wang1, Ting Zhao1, Gui-Nv He1, Xue-Rui Zhu1, Ying Zhang1, Jiu-Yan Han4, Dian-Ling Huang3. 1. Department of Neurology, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Henan Province, China. 2. Department of Pharmacy, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Henan Province, China. 3. Department of Computer Network Information Center, Chinese Academy of Sciences, Beijing, China. 4. Department of Clinical Medicine, Zhengzhou University, Henan Province, China.
Abstract
AIMS: To predict the probability of a seizure-free (SF) state in patients with epilepsy (PWEs) after treatment with levetiracetam and to identify the clinical and electroencephalographic (EEG) factors that affect outcomes. METHODS: Retrospective analysis of PWEs treated with levetiracetam for 3 years identified 22 patients who were SF and 24 who were not. Before starting levetiracetam, 11 clinical factors and four EEG features (sample entropy of α, β, θ, δ) were identified. Overall, 80% of each the two groups were chosen to establish a support vector machine (SVM) model with 5-fold cross-validation, hold-out validation and jack-knife validation. The other 20% were used to predict the efficacy of levetiracetam. The mean impact value (MIV) algorithm was used to rank the relativity between factors and outcomes. RESULTS: Compared with SF patients, not SF patients displayed a specific decrease in EEG sample entropy in α band from the F4 channel, β band from Fp2 and F8 channels, θ band from C3 channel (P < 0.05). The SVM model based on the clinical and EEG features yielded 72.2% accuracy of 5-fold cross-validation, 75.0% accuracy of jack-knife validation, 67.7% accuracy of hold-out validation in the training set and had a high prediction accuracy of 90% in test set (sensitivity was 100%, area under the receiver operating characteristic curve was 0.96). The feature of β band from Fp2 weighs heavily in the prediction model according to the mean impact value algorithm. CONCLUSIONS: The efficacy of levetiracetam on newly diagnosed PWEs could be predicted using an SVM model, which could guide antiepileptic drug selection.
AIMS: To predict the probability of a seizure-free (SF) state in patients with epilepsy (PWEs) after treatment with levetiracetam and to identify the clinical and electroencephalographic (EEG) factors that affect outcomes. METHODS: Retrospective analysis of PWEs treated with levetiracetam for 3 years identified 22 patients who were SF and 24 who were not. Before starting levetiracetam, 11 clinical factors and four EEG features (sample entropy of α, β, θ, δ) were identified. Overall, 80% of each the two groups were chosen to establish a support vector machine (SVM) model with 5-fold cross-validation, hold-out validation and jack-knife validation. The other 20% were used to predict the efficacy of levetiracetam. The mean impact value (MIV) algorithm was used to rank the relativity between factors and outcomes. RESULTS: Compared with SF patients, not SF patients displayed a specific decrease in EEG sample entropy in α band from the F4 channel, β band from Fp2 and F8 channels, θ band from C3 channel (P < 0.05). The SVM model based on the clinical and EEG features yielded 72.2% accuracy of 5-fold cross-validation, 75.0% accuracy of jack-knife validation, 67.7% accuracy of hold-out validation in the training set and had a high prediction accuracy of 90% in test set (sensitivity was 100%, area under the receiver operating characteristic curve was 0.96). The feature of β band from Fp2 weighs heavily in the prediction model according to the mean impact value algorithm. CONCLUSIONS: The efficacy of levetiracetam on newly diagnosed PWEs could be predicted using an SVM model, which could guide antiepileptic drug selection.
Authors: Robert S Fisher; J Helen Cross; Jacqueline A French; Norimichi Higurashi; Edouard Hirsch; Floor E Jansen; Lieven Lagae; Solomon L Moshé; Jukka Peltola; Eliane Roulet Perez; Ingrid E Scheffer; Sameer M Zuberi Journal: Epilepsia Date: 2017-03-08 Impact factor: 5.864
Authors: Ewout W Steyerberg; Karel G M Moons; Danielle A van der Windt; Jill A Hayden; Pablo Perel; Sara Schroter; Richard D Riley; Harry Hemingway; Douglas G Altman Journal: PLoS Med Date: 2013-02-05 Impact factor: 11.069
Authors: Ishmael Amarreh; Mary E Meyerand; Carl Stafstrom; Bruce P Hermann; Rasmus M Birn Journal: Neuroimage Clin Date: 2014-03-29 Impact factor: 4.881
Authors: Johann de Jong; Ioana Cutcutache; Matthew Page; Sami Elmoufti; Cynthia Dilley; Holger Fröhlich; Martin Armstrong Journal: Brain Date: 2021-07-28 Impact factor: 13.501
Authors: Bin Wang; Xiong Han; Zongya Zhao; Na Wang; Pan Zhao; Mingmin Li; Yue Zhang; Ting Zhao; Yanan Chen; Zhe Ren; Yang Hong Journal: Front Med (Lausanne) Date: 2022-01-03