ArticleBiomedical engineering letters2018
The earth mover's distance and Bayesian linear discriminant analysis for epileptic seizure detection in scalp EEG.
Article in Biomedical engineering letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 35 citations in OpenAlex.
- Epileptic Seizure Detection Using Machine Learning: A Systematic Review and Meta-Analysis.Brain sciences · 2025Review
- STEADYNet: Spatiotemporal EEG analysis for dementia detection using convolutional neural network.Cognitive neurodynamics · 2024Article
- Combining data augmentation and deep learning for improved epilepsy detection.Frontiers in neurology · 2024Article
- Characteristic analysis of epileptic brain network based on attention mechanism.Scientific reports · 2023Article
- Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Automatic epileptic seizure detection based on persistent homology.Frontiers in physiology · 2023Article
- Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals.Frontiers in systems neuroscience · 2021Review
- EEG microstate features for schizophrenia classification.PloS one · 2021Article
- InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
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Abstract
Since epileptic seizure is unpredictable and paroxysmal, an automatic system for seizure detecting could be of great significance and assistance to patients and medical staff. In this paper, a novel method is proposed for multichannel patient-specific seizure detection applying the earth mover's distance (EMD) in scalp EEG. Firstly, the wavelet decomposition is executed to the original EEGs with five scales, the scale 3, 4 and 5 are selected and transformed into histograms and afterwards the distances between histograms in pairs are computed applying the earth mover's distance as effective features. Then, the EMD features are sent to the classifier based on the Bayesian linear discriminant analysis (BLDA) for classification, and an efficient postprocessing procedure is applied to improve the detection system precision, finally. To evaluate the performance of the proposed method, the CHB-MIT scalp EEG database with 958 h EEG recordings from 23 epileptic patients is used and a relatively satisfactory detection rate is achieved with the average sensitivity of 95.65% and false detection rate of 0.68/h. The good performance of this algorithm indicates the potential application for seizure monitoring in clinical practice.
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