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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2503.19949 |
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| _version_ | 1866912660323303424 |
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| author | Zuev, Valerii A. Salmagambetova, Elena G. Djakov, Stepan N. Utkin, Lev V. |
| author_facet | Zuev, Valerii A. Salmagambetova, Elena G. Djakov, Stepan N. Utkin, Lev V. |
| contents | Epilepsy is typically diagnosed through electroencephalography (EEG) and long-term video-EEG (vEEG) monitoring. The manual analysis of vEEG recordings is time-consuming, necessitating automated tools for seizure detection. Recent advancements in machine learning have shown promise in real-time seizure detection and prediction using EEG and video data. However, diversity of seizure symptoms, markup ambiguities, and limited availability of multimodal datasets hinder progress. This paper reviews the latest developments in automated video-EEG analysis and discusses the integration of multimodal data. We also propose a novel pipeline for treatment effect estimation from vEEG data using concept-based learning, offering a pathway for future research in this domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19949 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges Zuev, Valerii A. Salmagambetova, Elena G. Djakov, Stepan N. Utkin, Lev V. Image and Video Processing Machine Learning Epilepsy is typically diagnosed through electroencephalography (EEG) and long-term video-EEG (vEEG) monitoring. The manual analysis of vEEG recordings is time-consuming, necessitating automated tools for seizure detection. Recent advancements in machine learning have shown promise in real-time seizure detection and prediction using EEG and video data. However, diversity of seizure symptoms, markup ambiguities, and limited availability of multimodal datasets hinder progress. This paper reviews the latest developments in automated video-EEG analysis and discusses the integration of multimodal data. We also propose a novel pipeline for treatment effect estimation from vEEG data using concept-based learning, offering a pathway for future research in this domain. |
| title | Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2503.19949 |