Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis

Abstract

Background: Real-time monitoring of pediatric epileptic seizures poses a significant challenge in clinical practice. In recent years, machine learning (ML) has attracted substantial attention from researchers for diagnosing and treating neurological diseases, leading to its application for detecting pediatric epileptic seizures. However, systematic evidence substantiating its feasibility remains limited.

Objective: This systematic review aimed to consolidate the existing evidence regarding the effectiveness of ML in monitoring pediatric epileptic seizures with an effort to provide an evidence-based foundation for the development and enhancement of intelligent tools in the future.

Methods: We conducted a systematic search of the PubMed, Cochrane, Embase, and Web of Science databases for original studies focused on the detection of pediatric epileptic seizures using ML, with a cutoff date of August 27, 2023. The risk of bias in eligible studies was assessed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2). Meta-analyses were performed to evaluate the C-index and the diagnostic 4-grid table, using a bivariate mixed-effects model for the latter. We also examined publication bias for the C-index by using funnel plots and the Egger test.

Results: This systematic review included 28 original studies, with 15 studies on ML and 13 on deep learning (DL). All these models were based on electroencephalography data of children. The pooled C-index, sensitivity, specificity, and accuracy of ML in the training set were 0.76 (95% CI 0.69-0.82), 0.77 (95% CI 0.73-0.80), 0.74 (95% CI 0.70-0.77), and 0.75 (95% CI 0.72-0.77), respectively. In the validation set, the pooled C-index, sensitivity, specificity, and accuracy of ML were 0.73 (95% CI 0.67-0.79), 0.88 (95% CI 0.83-0.91), 0.83 (95% CI 0.71-0.90), and 0.78 (95% CI 0.73-0.82), respectively. Meanwhile, the pooled C-index of DL in the validation set was 0.91 (95% CI 0.88-0.94), with sensitivity, specificity, and accuracy being 0.89 (95% CI 0.85-0.91), 0.91 (95% CI 0.88-0.93), and 0.89 (95% CI 0.86-0.92), respectively.

Conclusions: Our systematic review demonstrates promising accuracy of artificial intelligence methods in epilepsy detection. DL appears to offer higher detection accuracy than ML. These findings support the development of DL-based early-warning tools in future research.

Trial registration: PROSPERO CRD42023467260; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023467260.

Keywords: EEG; children; deep learning; detection; electroencephalogram; epilepsy; epileptic seizures; machine learning; pediatrics.

Figure 1

Literature selection flow chart.

Figure 2

Risk of bias (A) graph and (B) summary.

Figure 3

Forest and funnel plots of machine learning models for detecting seizures in children. The presence of repeated authors in the literature arises from the development of multiple machine learning models. (A) Forest plot illustrating the C-index summarization for the training set. (B) Forest plot illustrating the C-index summarization for the validation set. (C) Funnel plot illustrating the C-index for training set. (D) Funnel plot illustrating the C-index for the validation set. AdaBoost: adaptive boosting; ANN: artificial neural network; GBDT: gradient boosting decision tree; LDA: linear discriminant analysis; LR: logistic regression; NB: naive Bayes; RF: random forest; SVM: support vector machine; XGBoost: extreme gradient boosting.

Figure 4

The forest plot shows the sensitivity, specificity, and accuracy of machine learning models in detecting seizures in children. The presence of repeated authors in the literature arises from the development of multiple machine learning models. (A) Sensitivity and specificity of the training set, (B) Sensitivity and specificity of the validation set, and (C) Accuracy of the machine learning models for both the training set and the validation set post summarization. AdaBoost: adaptive boosting; ANN: artificial neural network; DT: decision tree; GBDT: gradient boosting decision tree; KNN: k-nearest neighbors; LR: logistic regression; SVM: support vector machine; RF: random forest; XGBoost: extreme gradient boosting.

Figure 5

Forest and funnel plots of the C-index for the validation set for deep learning models for detecting seizures in children. The presence of repeated authors in the literature arises from the development of multiple deep learning models. (A) Forest plot illustrating the C-index summarization for the validation set. (B) Funnel plot illustrating the C-index for the validation set.

Figure 6

The forest plot shows the sensitivity, specificity, and accuracy of deep learning models in detecting seizures in children. The presence of repeated authors in the literature arises from the development of multiple deep learning models. (A) The application of deep learning for seizure detection in children demonstrates the sensitivity and specificity of ensemble methods and forest plots; (B) Accuracy of the deep learning validation set post summarization.