Personalized prediction model for seizure‐free epilepsy with levetiracetam therapy: a retrospective data analysis using support vector machine

Zhang, Jia‐hui; Han, Xing; Zhao, Hongwei; Zhao, Di; Wang, Na; Zhao, Ting; He, Guinv; Zhu, Xuerui; Zhang, Ying; Han, Jiuyan; Huang, Dianling

doi:10.1111/bcp.13720

Cited by 43 publications

(29 citation statements)

References 42 publications

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“…CENPH scores were validated at two external epilepsy centers, and showed good discrimination, calibration, predictive accuracy and clinical values. Zhang et al [31] constructed an SVM model based on clinical features and EEG Sample Entropy with good performance. However, the sample was very small (46 patients) and the application of the model was limited to predicting the possibility of SF with levetiracetam treatment.…”

Section: Discussionmentioning

confidence: 99%

“…A few studies have been able to predict AEDs response based on SVM. Our team previously built a personalized prediction model for seizurefree(SF) epilepsy with levetiracetam therapy [31]. However, there are several limits inherent to previous studies, including examining only a single type of drug or certain kind of epilepsy, or having too small a sample size.…”

Section: Introductionmentioning

confidence: 99%

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An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features

et al. 2021

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Objective Although the use of antiepileptic drugs (AEDs) is routine, 30-40% of patients with epilepsy (PWEs) experience drug resistance. Thus, early identification of AED resistance will help optimize treatment regimens and improve patients' prognoses. However, there have been few studies on this topic to date. Here, we try to establish an integrative prediction model of AED resistance for drug-naive PWEs, and to identify the clinical and Electroencephalogram (EEG) factors that affect their outcomes. Methods One hundred sixty-four PWEs naive to AEDs treated at a tertiary care center from January 2014 to June 2020 were retrospectively analyzed. A total of 113 of these patients were well controlled and 53 were drug refractory with regular AED treatment for more than one year. Eighty clinical characteristics and 684 EEG functional connectivity variables based on phase lag index before drug initiation were identified. Overall, 80% of each group was chosen to establish a support vector machine (SVM) model with ten-fold cross validation, and the other 20% were used to evaluate the model's performance. Absolute weight value was used to rank the features that had impacts on classification. Results An integrative algorithm was modeled to predict AED resistance for drug-naive PWEs by SVM based on clinical characteristics and EEG functional connectivity values. The model had an accuracy of 94% [95% confidence interval (CI) 0.85-1.0], sensitivity of 95% [95% CI 0.82-1.0], specificity of 93% [95% CI 0.77-1.0], and an area under the curve (AUC) of 0.98 [95% CI 0.91-1.0]. The p values of accuracy, sensitivity specificity and AUC were calculated as 0.001, 0.001, 0.01 and 0.001, respectively. The δ band fromT4-FZ and T3-PZ, α band from T3-T6 and β band from F7-CZ and FP2-F3 were the top five EEG features that impacted the SVM classifier. ConclusionWe constructed an integrative prediction algorithm of AED resistance for drug-naive PWEs. Its utility in clinical settings should be evaluated in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features

et al. 2021

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“…Several studies have developed prediction models for several types of seizure using important predictive factors and various analysis methods, such as frequency-based methods using EEG signals, the Least Absolute Shrinkage and Selection Operator (LASSO) regression, nonlinear dynamics (chaos), and logistic regression and machine learning models such as k-NN, SVM and the deep learning classifier. [17][18][19][20][33][34][35] Nonetheless, the superiority of the application of ML algorithms over conventional logistic regression models to predict seizure remains a controversial assumption. The review of literature did not show any studies that had managed to forecast seizure caused by tramadol poisoning.…”

Section: Discussionmentioning

confidence: 99%

Machine learning algorithms to predict seizure due to acute tramadol poisoning

et al. 2021

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Introduction: This study was designed to develop and evaluate machine learning algorithms for predicting seizure due to acute tramadol poisoning, identifying high-risk patients and facilitating appropriate clinical decision-making. Methods: Several characteristics of acute tramadol poisoning cases were collected in the Emergency Department (ED) (2013–2019). After selecting important variables in random forest method, prediction models were developed using the Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and K-Nearest Neighbor (K-NN) algorithms. Area Under the Curve (AUC) and other diagnostic criteria were used to assess performance of models. Results: In 909 patients, 544 (59.8%) experienced seizures. The important predictors of seizure were sex, pulse rate, arterial blood oxygen pressure, blood bicarbonate level and pH. SVM (AUC = 0.68), NB (AUC = 0.71) and ANN (AUC = 0.70) models outperformed k-NN model (AUC = 0.58). NB model had a higher sensitivity and negative predictive value and k-NN model had higher specificity and positive predictive values than other models. Conclusion: A perfect prediction model may help improve clinicians’ decision-making and clinical care at EDs in hospitals and medical settings. SVM, ANN and NB models had no significant differences in the performance and accuracy; however, validated logistic regression (LR) was the superior model for predicting seizure due to acute tramadol poisoning.

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“…For structural dependence models SVM, the special principle and algorithm is to find a hyper-plane in high dimensional space with feature vectors from the samples. Therefore, SVM was used in this study as an effective prediction model for classification of variables using the limited sample ( Zhang et al, 2018 ). Radial basis function (RBF) was used as the SVM kernel function, which is the most common kernel function used to map data into a space ( Gromski et al, 2014 ).…”

Section: Methodsmentioning

confidence: 99%

Neural Tracking of Sound Rhythms Correlates With Diagnosis, Severity, and Prognosis of Disorders of Consciousness

Zou

et al. 2021

Front. Neurosci.

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Effective diagnosis and prognosis of patients with disorders of consciousness (DOC) provides a basis for family counseling, decision-making, and the design of rehabilitation programs. However, effective and objective bedside evaluation is a challenging problem. In this study, we explored electroencephalography (EEG) response tracking sound rhythms as potential neural markers for DOC evaluation. We analyzed the responses to natural speech and tones modulated at 2 and 41 Hz. At the population level, patients with positive outcomes (DOC-P) showed higher cortical synchronization to modulated tones at 41 Hz compared with patients with negative outcomes (DOC-N). At the individual level, phase coherence to modulated tones at 41 Hz was significantly correlated with Coma Recovery Scale-Revised (CRS-R) and Glasgow Outcome Scale-Extended (GOS-E) scores. Furthermore, SVM classifiers, trained using phase coherences in higher frequency bands or combination of the low frequency aSSR and speech tracking responses, performed very well in diagnosis and prognosis of DOC. These findings show that EEG response to auditory rhythms is a potential tool for diagnosis, severity, and prognosis of DOC.

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Personalized prediction model for seizure‐free epilepsy with levetiracetam therapy: a retrospective data analysis using support vector machine

Abstract: The efficacy of levetiracetam on newly diagnosed PWEs could be predicted using an SVM model, which could guide antiepileptic drug selection.

Cited by 43 publications

References 42 publications

An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features

An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features

Machine learning algorithms to predict seizure due to acute tramadol poisoning

Neural Tracking of Sound Rhythms Correlates With Diagnosis, Severity, and Prognosis of Disorders of Consciousness

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