Co-ReaSON: EEG-based Onset Detection of Focal Epileptic Seizures with Multimodal Feature Representations

Wed, 01 May 2024 00:00:00 GMT

Co-ReaSON: EEG-based Onset Detection of Focal Epileptic Seizures with Multimodal Feature Representations Kumar, Uttam; Yu, Ran; Wenzel, Michael; Demidova, Elena Yang, De-Nian; Xie, Xing; Tseng, Vincent S.; Pei, Jian; Huang, Jen-Wei; Lin, Jerry Chun-Wei Early detection of an epileptic seizure's onset is crucial to reduce the impact of seizures on the patient's health. The Electroencephalogram (EEG) has been widely used in clinical epileptology for continuous, long-term measurement of electrical activity in the brain. Despite numerous EEG-based approaches employing diverse models and feature extraction methods for seizure detection, these methods rarely tackle the more challenging task of early detection of the seizure onset, especially as only a few EEG channels are impacted at the onset, and the seizure evidence is minimal. Furthermore, EEG-based seizure onset detection remains challenging due to the sparse, imbalanced, and noisy data, as well as the complexity posed by the diverse nature of epileptic seizures in patients. In this paper, we propose Co-ReaSON – a novel approach towards early detection of focal seizure onsets by considering the onset-specific increase in spatio-temporal correlations across the EEG channels observed over a range of multimodal EEG feature representations, combined in a ResNet18-based model architecture. Evaluation on a real-world dataset demonstrates that Co-ReaSON outperforms the state-of-the-art baselines in focal seizure onset detection by at least 5 percent points regarding the macro-average F1-score.

Leveraging Synthetically Generated Data for Real Estate Document Classification

Mon, 01 Dec 2025 00:00:00 GMT

Leveraging Synthetically Generated Data for Real Estate Document Classification Deußer, Tobias; Ramien, Gregor; Weber, Nico; Meidinger, Maximilian; Hahnbück, Max; Bauckhage, Christian; Sifa, Rafet Document classification in regulated domains like law, finance, or real estate is hindered by the scarcity of labeled data and strict privacy constraints. This paper presents a pipeline for synthetically generating training data for document classifiers using a combination of domain-specific templates, large language models, and data augmentation techniques. Focusing on two key document types relevant to real estate workflows, Child Support Certificate and Refurbishment Roadmap, we construct realistic multi-page documents and generate negative classes using LLM-generated distractors. We train a BERT-based classifier on this synthetic dataset and evaluate it on real-world OCR-extracted documents, achieving strong performance despite the absence of real documents in training. Our findings highlight the feasibility of using synthetic data to overcome annotation bottlenecks and pave the way for broader applications in privacy-sensitive industries.

Publikationen

Co-ReaSON: EEG-based Onset Detection of Focal Epileptic Seizures with Multimodal Feature Representations

Leveraging Synthetically Generated Data for Real Estate Document Classification