Detection of nocturnal epileptic seizures using a wearable armband: A deep learning approach combining accelerometry and photoplethysmography signals Article Swipe
Chunjiao Dong
,
Johannes van Dijk
,
Ronald M. Aarts
,
Yunfeng Wang
,
Xi Long
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.cmpb.2025.109087
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.cmpb.2025.109087
Our two-step approach enables accurate, long-term detection of severe nocturnal seizures in home settings. The wearable system provides a practical solution for continuous monitoring and real-time alerts, thus potentially reducing SUDEP risk and improving patient safety, fulfilling an urgent unmet need in epilepsy care. Furthermore, by enabling long-term home monitoring, this system may help assess the relationship between seizure events and lifestyle-related triggers such as sleep deprivation, stress, physical exertion, or alcohol consumption, thereby supporting the development of personalized preventive strategies.
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Detection of nocturnal epileptic seizures using a wearable armband: A deep learning approach combining accelerometry and photoplethysmography signalsWork title
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articleOpenAlex work type
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2025Year of publication
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2025-09-29Full publication date if available
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Chunjiao Dong, Johannes van Dijk, Ronald M. Aarts, Yunfeng Wang, Xi LongList of authors in order
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https://doi.org/10.1016/j.cmpb.2025.109087Publisher landing page
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.cmpb.2025.109087Direct OA link when available
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| abstract_inverted_index.and | 24, 32, 60 |
| abstract_inverted_index.for | 21 |
| abstract_inverted_index.may | 52 |
| abstract_inverted_index.the | 55, 75 |
| abstract_inverted_index.help | 53 |
| abstract_inverted_index.home | 12, 48 |
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| abstract_inverted_index.such | 63 |
| abstract_inverted_index.this | 50 |
| abstract_inverted_index.thus | 27 |
| abstract_inverted_index.SUDEP | 30 |
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| abstract_inverted_index.alcohol | 71 |
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| abstract_inverted_index.solution | 20 |
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| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.47645107 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |