SeizeIT2: Wearable Dataset Of Patients With Focal Epilepsy
Abstract The increasing technological advancements towards miniaturized physiological measuring devices have enabled continuous monitoring of epileptic patients outside of specialized environments. The large amounts of data that can be recorded with such devices hold significant potential for develo...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05580-x |
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| author | Miguel Bhagubai Christos Chatzichristos Lauren Swinnen Jaiver Macea Jingwei Zhang Lieven Lagae Katrien Jansen Andreas Schulze-Bonhage Francisco Sales Benno Mahler Yvonne Weber Wim Van Paesschen Maarten De Vos |
| author_facet | Miguel Bhagubai Christos Chatzichristos Lauren Swinnen Jaiver Macea Jingwei Zhang Lieven Lagae Katrien Jansen Andreas Schulze-Bonhage Francisco Sales Benno Mahler Yvonne Weber Wim Van Paesschen Maarten De Vos |
| author_sort | Miguel Bhagubai |
| collection | DOAJ |
| description | Abstract The increasing technological advancements towards miniaturized physiological measuring devices have enabled continuous monitoring of epileptic patients outside of specialized environments. The large amounts of data that can be recorded with such devices hold significant potential for developing automated seizure detection frameworks. In this work, we present SeizeIT2, the first open dataset of wearable data recorded in patients with focal epilepsy. The dataset comprises more than 11,000 hours of multimodal data, including behind-the-ear electroencephalography, electrocardiography, electromyography and movement (accelerometer and gyroscope) data. The dataset contains 883 focal seizures recorded from 125 patients across five different European Epileptic Monitoring Centers. We present a suggestive training/validation split to propel the development of AI methodologies for seizure detection, as well as two benchmark approaches and evaluation metrics. The dataset can be accessed on OpenNeuro and is stored in Brain Imaging Data Structure (BIDS) format. |
| format | Article |
| id | doaj-art-b4461b0d68014a9cb04e887eb6caab9e |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-b4461b0d68014a9cb04e887eb6caab9e2025-08-20T04:01:43ZengNature PortfolioScientific Data2052-44632025-07-0112111010.1038/s41597-025-05580-xSeizeIT2: Wearable Dataset Of Patients With Focal EpilepsyMiguel Bhagubai0Christos Chatzichristos1Lauren Swinnen2Jaiver Macea3Jingwei Zhang4Lieven Lagae5Katrien Jansen6Andreas Schulze-Bonhage7Francisco Sales8Benno Mahler9Yvonne Weber10Wim Van Paesschen11Maarten De Vos12Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU LeuvenDepartment of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU LeuvenLaboratory for Epilepsy Research, UZ LeuvenLaboratory for Epilepsy Research, UZ LeuvenDepartment of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU LeuvenDepartment of Pediatric Neurology, UZ LeuvenDepartment of Pediatric Neurology, UZ LeuvenEpilepsy Center, University Medical Center, Freiburg UniversityEpilepsy Reference Center, Coimbra University HospitalDepartment of Neurology, Karolinska University HospitalDepartment of Epileptology and Neurology, RWTH University of AachenLaboratory for Epilepsy Research, UZ LeuvenDepartment of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU LeuvenAbstract The increasing technological advancements towards miniaturized physiological measuring devices have enabled continuous monitoring of epileptic patients outside of specialized environments. The large amounts of data that can be recorded with such devices hold significant potential for developing automated seizure detection frameworks. In this work, we present SeizeIT2, the first open dataset of wearable data recorded in patients with focal epilepsy. The dataset comprises more than 11,000 hours of multimodal data, including behind-the-ear electroencephalography, electrocardiography, electromyography and movement (accelerometer and gyroscope) data. The dataset contains 883 focal seizures recorded from 125 patients across five different European Epileptic Monitoring Centers. We present a suggestive training/validation split to propel the development of AI methodologies for seizure detection, as well as two benchmark approaches and evaluation metrics. The dataset can be accessed on OpenNeuro and is stored in Brain Imaging Data Structure (BIDS) format.https://doi.org/10.1038/s41597-025-05580-x |
| spellingShingle | Miguel Bhagubai Christos Chatzichristos Lauren Swinnen Jaiver Macea Jingwei Zhang Lieven Lagae Katrien Jansen Andreas Schulze-Bonhage Francisco Sales Benno Mahler Yvonne Weber Wim Van Paesschen Maarten De Vos SeizeIT2: Wearable Dataset Of Patients With Focal Epilepsy Scientific Data |
| title | SeizeIT2: Wearable Dataset Of Patients With Focal Epilepsy |
| title_full | SeizeIT2: Wearable Dataset Of Patients With Focal Epilepsy |
| title_fullStr | SeizeIT2: Wearable Dataset Of Patients With Focal Epilepsy |
| title_full_unstemmed | SeizeIT2: Wearable Dataset Of Patients With Focal Epilepsy |
| title_short | SeizeIT2: Wearable Dataset Of Patients With Focal Epilepsy |
| title_sort | seizeit2 wearable dataset of patients with focal epilepsy |
| url | https://doi.org/10.1038/s41597-025-05580-x |
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