ArticleFrontiers in human neuroscience2019
Detection Analysis of Epileptic EEG Using a Novel Random Forest Model Combined With Grid Search Optimization.
Article in Frontiers in human neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review.Computational intelligence and neuroscience · 2022Pooled it
- Effect of Emotional States on EEG-Based Biometric Identification: A Comparative Study of Classifiers.Bioengineering (Basel, Switzerland) · 2026Article
- Decoding Chinese speech across multiple neural conditions via EEG: dataset construction and interpretability driven spatial optimization.Frontiers in psychology · 2026Article
- RDPNet: A Multi-Scale Residual Dilated Pyramid Network with Entropy-Based Feature Fusion for Epileptic EEG Classification.Entropy (Basel, Switzerland) · 2025Article
- Highly efficient stacking ensemble learning model for automated keratoconus screening.Eye and vision (London, England) · 2025Article
- Tumour-infiltrating immune cells as a novel prognostic model for bladder cancer.Discover oncology · 2025Article
- Assistive Artificial Intelligence in Epilepsy and Its Impact on Epilepsy Care in Low- and Middle-Income Countries.Brain sciences · 2025Review
- EEG-based epilepsy detection using CNN-SVM and DNN-SVM with feature dimensionality reduction by PCA.Scientific reports · 2025Article
- Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification.Sensors (Basel, Switzerland) · 2025Review
- SPINDILOMETER: a model describing sleep spindles on EEG signals for polysomnography.Physical and engineering sciences in medicine · 2024Article
- Smart decision support system for keratoconus severity staging using corneal curvature and thinnest pachymetry indices.Eye and vision (London, England) · 2024Article
- Assessment of rainfall-derived inflow and infiltration in sewer systems with machine learning approaches.Water science and technology : a journal of the International Association on Water Pollution Research · 2024Article
- Article
- Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis.Frontiers in pharmacology · 2024Article
- A comparison of random survival forest and Cox regression for prediction of mortality in patients with hemorrhagic stroke.BMC medical informatics and decision making · 2023Article
- Automated recognition of epilepsy from EEG signals using a combining space-time algorithm of CNN-LSTM.Scientific reports · 2023Article
- A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates.Sensors (Basel, Switzerland) · 2023Article
- An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy.BMC medical informatics and decision making · 2023Article
- Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges.Diagnostics (Basel, Switzerland) · 2023Review
- Application of Machine Learning in Epileptic Seizure Detection.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the automatic detection of epileptic seizures, the monitoring of critically ill patients with time varying EEG signals is an essential procedure in intensive care units. There is an increasing interest in using EEG analysis to detect seizure, and in this study we aim to get a better understanding of how to visualize the information in the EEG time-frequency feature, and design and train a novel random forest algorithm for EEG decoding, especially for multiple-levels of illness. Here, we propose an automatic detection framework for epileptic seizure based on multiple time-frequency analysis approaches; it involves a novel random forest model combined with grid search optimization. The short-time Fourier transformation visualizes seizure features after normalization. The dimensionality of features is reduced through principal component analysis before feeding them into the classification model. The training parameters are optimized using grid search optimization to improve detection performance and diagnostic accuracy by in the recognition of three different levels epileptic of conditions (healthy subjects, seizure-free intervals, seizure activity). Our proposed model was used to classify 500 samples of raw EEG data, and multiple cross-validations were adopted to boost the modeling accuracy. Experimental results were evaluated by an accuracy, a confusion matrix, a receiver operating characteristic curve, and an area under the curve. The evaluations indicated that our model achieved the more effective classification than some previous typical methods. Such a scheme for computer-assisted clinical diagnosis of seizures has a potential guiding significance, which not only relieves the suffering of patient with epilepsy to improve quality of life, but also helps neurologists reduce their workload.
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