ArticleScientific reports2020
Comparison of different input modalities and network structures for deep learning-based seizure detection.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Diffusion Model-Based Multi-Channel EEG Representation and Forecasting for Early Epileptic Seizure Warning.Interdisciplinary sciences, computational life sciences · 2026Article
- RDPNet: A Multi-Scale Residual Dilated Pyramid Network with Entropy-Based Feature Fusion for Epileptic EEG Classification.Entropy (Basel, Switzerland) · 2025Article
- Assistive Artificial Intelligence in Epilepsy and Its Impact on Epilepsy Care in Low- and Middle-Income Countries.Brain sciences · 2025Review
- Semi-automated seizure detection using interpretable machine learning models.Research square · 2024Article
- Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images.Sensors (Basel, Switzerland) · 2024Article
- Residual and bidirectional LSTM for epileptic seizure detection.Frontiers in computational neuroscience · 2024Article
- PSAEEGNet: pyramid squeeze attention mechanism-based CNN for single-trial EEG classification in RSVP task.Frontiers in human neuroscience · 2024Article
- Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis.Biological cybernetics · 2023Article
- Capturing the power of seizures: an empirical mode decomposition analysis of epileptic activity in the mouse hippocampus.Frontiers in molecular neuroscience · 2023Article
- Prediction of Short-Term Mortality of Cardiac Care Unit Patients Using Image-Transformed ECG Waveforms.IEEE journal of translational engineering in health and medicine · 2023Article
- Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity.Scientific reports · 2022Article
- A medium-weight deep convolutional neural network-based approach for onset epileptic seizures classification in EEG signals.Brain and behavior · 2022Article
- Modeling of a Generic Edge Computing Application Design.Sensors (Basel, Switzerland) · 2021Article
- A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal.Brain sciences · 2021Review
- Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings.Scientific reports · 2021Article
- Automatic seizure detection based on imaged-EEG signals through fully convolutional networks.Scientific reports · 2020Article
- Accurate detection of spontaneous seizures using a generalized linear model with external validation.Epilepsia · 2020Article
- How Deep Learning Solved My Seizure Detection Problems.Epilepsy currents · 2020Article
- Artificial Intelligence and Computational Approaches for Epilepsy.Journal of epilepsy research · 2020Review
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The manual review of an electroencephalogram (EEG) for seizure detection is a laborious and error-prone process. Thus, automated seizure detection based on machine learning has been studied for decades. Recently, deep learning has been adopted in order to avoid manual feature extraction and selection. In the present study, we systematically compared the performance of different combinations of input modalities and network structures on a fixed window size and dataset to ascertain an optimal combination of input modalities and network structures. The raw time-series EEG, periodogram of the EEG, 2D images of short-time Fourier transform results, and 2D images of raw EEG waveforms were obtained from 5-s segments of intracranial EEGs recorded from a mouse model of epilepsy. A fully connected neural network (FCNN), recurrent neural network (RNN), and convolutional neural network (CNN) were implemented to classify the various inputs. The classification results for the test dataset showed that CNN performed better than FCNN and RNN, with the area under the curve (AUC) for the receiver operating characteristics curves ranging from 0.983 to 0.984, from 0.985 to 0.989, and from 0.989 to 0.993 for FCNN, RNN, and CNN, respectively. As for input modalities, 2D images of raw EEG waveforms yielded the best result with an AUC of 0.993. Thus, CNN can be the most suitable network structure for automated seizure detection when applied to the images of raw EEG waveforms, since CNN can effectively learn a general spatially-invariant representation of seizure patterns in 2D representations of raw EEG.
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