ArticleBrain informatics2018
Automated epileptic seizures detection using multi-features and multilayer perceptron neural network.
Article in Brain informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Directed graph neural networks with partial directed coherence for seizure prediction and epileptogenic network characterization.iScience · 2026Article
- Hilbert-domain sub-band feature framework for EEG-based seizure detection.Frontiers in computational neuroscience · 2026Article
- Effectual seizure detection using MBBF-GPSO with CNN network.Cognitive neurodynamics · 2024Article
- Predicting rectal cancer prognosis from histopathological images and clinical information using multi-modal deep learning.Frontiers in oncology · 2024Article
- A Novel Approach for Multichannel Epileptic Seizure Classification Based on Internet of Things Framework Using Critical Spectral Verge Feature Derived from Flower Pollination Algorithm.Sensors (Basel, Switzerland) · 2022Article
- Neuromorphic Architecture Accelerated Automated Seizure Detection in Multi-Channel Scalp EEG.Sensors (Basel, Switzerland) · 2022Article
- Graph Eigen Decomposition-Based Feature-Selection Method for Epileptic Seizure Detection Using Electroencephalography.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
7 authors.
Funding
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Abstract
Detection of epileptic seizure activities from long-term multi-channel electroencephalogram (EEG) signals plays a significant role in the timely treatment of the patients with epilepsy. Visual identification of epileptic seizure in long-term EEG is cumbersome and tedious for neurologists, which might also lead to human error. Therefore, an automated tool for accurate detection of seizures in a long-term multi-channel EEG is essential for the clinical diagnosis. This study proposes an algorithm using multi-features and multilayer perceptron neural network (MLPNN) classifier. After appropriate approval from the ethical committee, recordings of EEG data were collected from the Institute of Neurosciences, Ramaiah Memorial College and Hospital, Bengaluru. Initially, preprocessing was performed to remove the power-line noise and motion artifacts. Four features, namely power spectral density (Yule-Walker), entropy (Shannon and Renyi), and Teager energy, were extracted. The Wilcoxon rank-sum test and descriptive analysis ensure the suitability of the proposed features for pattern classification. Single and multi-features were fed to the MLPNN classifier to evaluate the performance of the study. The simulation results showed sensitivity, specificity, and false detection rate of 97.1%, 97.8%, and 1 h
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