ArticleComputational and mathematical methods in medicine2022
Epileptic Seizure Detection with Hybrid Time-Frequency EEG Input: A Deep Learning Approach.
Article in Computational and mathematical methods in medicine, 2022. 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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7 citing papers in PubMed.
- SinTransNet: an EEG-based deep learning framework for infantile epileptic spasms syndrome detection.BMC medical informatics and decision making · 2026Article
- IESS-FusionNet: Physiologically Inspired EEG-EMG Fusion with Linear Recurrent Attention for Infantile Epileptic Spasms Syndrome Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning: a review.Frontiers in neuroscience · 2025Review
- A novel epileptic seizure prediction model based on Cox-Stuart and Optuna.Frontiers in neurology · 2025Article
- A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning.Frontiers in neuroscience · 2024Review
- Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals-A Systematic Literature Review.Bioengineering (Basel, Switzerland) · 2022Review
- Computational Methods for Physiological Signal Processing and Data Analysis.Computational and mathematical methods in medicine · 2022Article
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6 authors.
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Abstract
The precise detection of epileptic seizure helps to prevent the serious consequences of seizures. As the electroencephalogram (EEG) reflects the brain activity of patients effectively, it has been widely used in epileptic seizure detection in the past decades. Recently, deep learning-based detection methods which automatically learn features from the EEG signals have attracted much attention. However, with deep learning-based detection methods, different input formats of EEG signals will lead to different detection performances. In this paper, we propose a deep learning-based epileptic seizure detection method with hybrid input formats of EEG signals, i.e., original EEG, Fourier transform of EEG, short-time Fourier transform of EEG, and wavelet transform of EEG. Convolutional neural networks (CNNs) are designed for extracting latent features from these inputs. A feature fusion mechanism is applied to integrate the learned features to generate a more stable syncretic feature for seizure detection. The experimental results show that our proposed hybrid method is effective to improve the seizure detection performance in few-shot scenarios.
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