ArticleSensors (Basel, Switzerland)2021
Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 80 citations in OpenAlex.
- Directed graph neural networks with partial directed coherence for seizure prediction and epileptogenic network characterization.iScience · 2026Article
- Hybrid quantum classical framework for electroencephalogram driven neurological processing in epileptic seizure taxonomy.Scientific reports · 2026Article
- Enhancing seizure prediction using a DC-SA-EBiLSTM framework with self-attention mechanism.Frontiers in neuroscience · 2026Article
- EEG based epileptic seizure detection using SVM fuzzy learning and metaheuristic optimization.Scientific reports · 2025Article
- A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection.BMC medical informatics and decision making · 2025Article
- EPIC-NET: EEG-based epilepsy classification and brain localization using Optuna wave-gated recurrent unit network.Frontiers in computational neuroscience · 2025Article
- Design of a hybrid AI network circuit for epilepsy detection with 97.5% accuracy and low cost-latency.Frontiers in physiology · 2025Article
- Neurostimulation for the Management of Epilepsy: Advances in Targeted Therapy.Current drug targets · 2025Review
- A deep hybrid CSAE-GRU framework with two-stage balancing for automatic epileptic seizure detection using EEG-derived features.Frontiers in neuroscience · 2025Article
- A Pathological Diagnosis Method for Fever of Unknown Origin Based on Multipath Hierarchical Classification: Model Design and Validation.JMIR formative research · 2024Article
- Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.Sensors (Basel, Switzerland) · 2024Article
- Fractal Geometry Meets Computational Intelligence: Future Perspectives.Advances in neurobiology · 2024Article
- Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time-Frequency EEG Images.Sensors (Basel, Switzerland) · 2023Article
- Brain-Computer Interface: The HOL-SSA Decomposition and Two-Phase Classification on the HGD EEG Data.Diagnostics (Basel, Switzerland) · 2023Article
- A Shallow Autoencoder Framework for Epileptic Seizure Detection in EEG Signals.Sensors (Basel, Switzerland) · 2023Article
- Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results.Sensors (Basel, Switzerland) · 2023Article
- M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity.Sensors (Basel, Switzerland) · 2022Article
- A Classification Model of EEG Signals Based on RNN-LSTM for Diagnosing Focal and Generalized Epilepsy.Sensors (Basel, Switzerland) · 2022Article
- Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals.Brain sciences · 2022Article
- Impact of EEG Frequency Bands and Data Separation on the Performance of Person Verification Employing Neural Networks.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epilepsy is a brain disorder disease that affects people's quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5-40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN-RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN-RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN-RNN classification procedure. The results revealed that the proposed CNN-RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.