ArticleScientific reports2021
Power efficient refined seizure prediction algorithm based on an enhanced benchmarking.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Single-channel EEG-based seizure prediction using deep learning.Scientific reports · 2026Article
- MLSPred-bench: Transforming electroencephalography (EEG) datasets into machine learning-ready epileptic seizure prediction benchmarks.MethodsX · 2025Article
- Utilizing Pretrained Vision Transformers and Large Language Models for Epileptic Seizure Prediction.2025 8th International Conference on Data Science and Machine Learning Applications · 2025Article
- Lightweight Transformer exhibits comparable performance to LLMs for Seizure Prediction: A case for light-weight models for EEG data.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2024Article
- Precise and low-power closed-loop neuromodulation through algorithm-integrated circuit co-design.Frontiers in neuroscience · 2024Article
- Discrimination of secondary hypsarrhythmias to Zika virus congenital syndrome and west syndrome based on joint moments and entropy measurements.Scientific reports · 2022Article
- The White Matter Functional Abnormalities in Patients with Transient Ischemic Attack: A Reinforcement Learning Approach.Neural plasticity · 2022Article
- Robustness of ML-Based Seizure Prediction Using Noisy EEG Data From Limited Channels.... International Conference on Distributed Computing in Sensor Systems and workshops. DCOSS (Conference)Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning techniques have led to significant advancements in seizure prediction research. However, corresponding used benchmarks are not uniform in published results. Moreover, inappropriate training and evaluation processes used in various work create overfitted models, making prediction performance fluctuate or unreliable. In this study, we analyzed the various data preparation methods, dataset partition methods in related works, and explained the corresponding impacts to the prediction algorithms. Then we applied a robust processing procedure that considers the appropriate sampling parameters and the leave-one-out cross-validation method to avoid possible overfitting and provide prerequisites for ease benchmarking. Moreover, a deep learning architecture takes advantage of a one-dimension convolutional neural network and a bi-directional long short-term memory network is proposed for seizure prediction. The architecture achieves 77.6% accuracy, 82.7% sensitivity, and 72.4% specificity, and it outperforms the indicators of other prior-art works. The proposed model is also hardware friendly; it has 6.274 k parameters and requires only 12.825 M floating-point operations, which is advantageous for memory and power constrained device implementations.
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