ArticleCognitive neurodynamics2018
Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.
Article in Cognitive neurodynamics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 104 citations in OpenAlex.
- Machine-learning methods for epilepsy diagnosis and therapeutic prevention: advances, setbacks, and opportunities.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- Advancing early detection of chromophobe renal cell carcinoma: a Bayesian optimization approach to machine learning models.Scientific reports · 2026Article
- Adaptive multi-scale phase-aware fusion network for EEG seizure recognition.Frontiers in neurology · 2025Article
- Article
- Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges.Diagnostics (Basel, Switzerland) · 2023Review
- An effective fusion model for seizure prediction: GAMRNN.Frontiers in neuroscience · 2023Article
- Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis.Frontiers in neuroscience · 2023Article
- Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI.Scientific reports · 2022Article
- Deep-layer motif method for estimating information flow between EEG signals.Cognitive neurodynamics · 2022Article
- LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals.Cognitive neurodynamics · 2022Article
- Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals.Sensors (Basel, Switzerland) · 2022Article
- Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches.Computational and mathematical methods in medicine · 2022Review
- [Research progress of epileptic seizure predictions based on electroencephalogram signals].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Review
- Sharp decrease in the Laplacian matrix rank of phase-space graphs: a potential biomarker in epilepsy.Cognitive neurodynamics · 2021Article
- Detecting prostate cancer using deep learning convolution neural network with transfer learning approach.Cognitive neurodynamics · 2020Article
- Medium- and long-term functional behavior evaluations in an experimental focal ischemic stroke mouse model.Cognitive neurodynamics · 2020Article
- The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method.Cognitive neurodynamics · 2020Article
- Seizure Prediction Model in Acute Tramadol Poisoning; a Derivation and Validation study.Archives of academic emergency medicine · 2020Article
- Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods.Cognitive neurodynamics · 2019Article
- Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis.Cognitive neurodynamics · 2019Article
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Authors and funding
1 author at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.