Curriculum Learning with Sampling Scheduler for Imbalanced EEG-Based Seizure Detection
Epileptic seizures pose serious health risks and significantly affect the quality of life for individuals with epilepsy, emphasizing the importance of accurate and timely detection. Despite advancements in electroencephalography (EEG) based seizure detection, class imbalance between seizure and non-...
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| Veröffentlicht in: | The ... International Winter Conference on Brain-Computer Interface S. 1 - 5 |
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24.02.2025
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| ISSN: | 2572-7672 |
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| Abstract | Epileptic seizures pose serious health risks and significantly affect the quality of life for individuals with epilepsy, emphasizing the importance of accurate and timely detection. Despite advancements in electroencephalography (EEG) based seizure detection, class imbalance between seizure and non-seizure classes remains a major challenge, often leading to biased machine learning models and reduced generalizability. To address this, we propose a sampling scheduler approach inspired by dynamic curriculum learning. Unlike conventional resampling techniques, our method gradually adjusts the sampling ratio between seizure and non-seizure samples during training, enabling the model to effectively learn from imbalanced data. Experiments on the children's hospital boston and the massachusetts institute of technology scalp EEG (CHB-MIT) dataset demonstrate that our approach outperforms conventional methods, achieving superior results in balanced accuracy, specificity, area under th curve, and geometirc mean. These findings highlight the potential of the sampling scheduler to address the imbalance problem and enhance EEG-based seizure detection. |
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| AbstractList | Epileptic seizures pose serious health risks and significantly affect the quality of life for individuals with epilepsy, emphasizing the importance of accurate and timely detection. Despite advancements in electroencephalography (EEG) based seizure detection, class imbalance between seizure and non-seizure classes remains a major challenge, often leading to biased machine learning models and reduced generalizability. To address this, we propose a sampling scheduler approach inspired by dynamic curriculum learning. Unlike conventional resampling techniques, our method gradually adjusts the sampling ratio between seizure and non-seizure samples during training, enabling the model to effectively learn from imbalanced data. Experiments on the children's hospital boston and the massachusetts institute of technology scalp EEG (CHB-MIT) dataset demonstrate that our approach outperforms conventional methods, achieving superior results in balanced accuracy, specificity, area under th curve, and geometirc mean. These findings highlight the potential of the sampling scheduler to address the imbalance problem and enhance EEG-based seizure detection. |
| Author | Nam, Hyeonyeong Choi, WooHyeok Kam, Tae-Eui Kim, Jun-Mo |
| Author_xml | – sequence: 1 givenname: WooHyeok surname: Choi fullname: Choi, WooHyeok email: woohyeok_choi@korea.ac.kr organization: Korea University,Dept. Artificial Intelligence,Seoul,South Korea – sequence: 2 givenname: Jun-Mo surname: Kim fullname: Kim, Jun-Mo email: wnsah1008@korea.ac.kr organization: Korea University,Dept. Artificial Intelligence,Seoul,South Korea – sequence: 3 givenname: Hyeonyeong surname: Nam fullname: Nam, Hyeonyeong email: hy_nam@korea.ac.kr organization: Korea University,Dept. Artificial Intelligence,Seoul,South Korea – sequence: 4 givenname: Tae-Eui surname: Kam fullname: Kam, Tae-Eui email: kamte@korea.ac.kr organization: Korea University,Dept. Artificial Intelligence,Seoul,South Korea |
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| Snippet | Epileptic seizures pose serious health risks and significantly affect the quality of life for individuals with epilepsy, emphasizing the importance of accurate... |
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| SubjectTerms | Accuracy Brain modeling Class Imbalance Curriculum Learning Data models Dynamic scheduling Electroen-cephalogram Electroencephalography Epilepsy Epileptic Seizure Hospitals Machine learning Sampling Scheduler Scalp Seizure Detection Training |
| Title | Curriculum Learning with Sampling Scheduler for Imbalanced EEG-Based Seizure Detection |
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