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Non-invasive Seizure Detection Using Wearable Wrist-worn Accelerometry and Deep Learning — arXiv:2610.05919

Source: arXiv
Original: https://arxiv.org/abs/2610.05919
License (as archived): CC0-1.0
Archived: 2026-10-06T05:27:16.256Z
SHA-256 of the archived text: e4a8054f0575cb1f0aca225d175d796c897f4ba3c3c755cd1ff7af5afcb53ef7
Stable URL of this page: https://metraipolis.com/documento/dc_ed4e374ba9fb

How to cite

Non-invasive Seizure Detection Using Wearable Wrist-worn Accelerometry and Deep Learning — arXiv:2610.05919 — arXiv, https://arxiv.org/abs/2610.05919 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ed4e374ba9fb (Metraipolis archive dc_ed4e374ba9fb, sha256 e4a8054f0575cb1f0aca225d175d796c897f4ba3c3c755cd1ff7af5afcb53ef7)

BibTeX

@misc{dced4e374ba9fb,
  title = {Non-invasive Seizure Detection Using Wearable Wrist-worn Accelerometry and Deep Learning — arXiv:2610.05919},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.05919},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ed4e374ba9fb; sha256 e4a8054f0575cb1f0aca225d175d796c897f4ba3c3c755cd1ff7af5afcb53ef7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.05919. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Seizure monitoring and detection are crucial for reducing the morbidity and mortality associated with seizures. Current epilepsy care, often involving expensive video-electroencephalography (VEEG) monitoring, requires specialized expertise and is limited to in-hospital settings, and intrusive in nature. Seizure diaries, on the other hand, suffer from unreliability due to under-reporting, leading to incorrect therapeutic decisions. Wearable non-invasive seizure detection may offer a more tolerable and feasible solution for long-term ambulatory monitoring. This study explores a wearable remote monitoring system utilizing a single wrist-worn accelerometer device and capable of detecting multiple types of seizures, including shorter duration events. We enrolled 79 patients under video-electroencephalography monitoring to wear accelerometer devices and collect data. Concurrent VEEG recordings were reviewed by board-certified epileptologists to produce annotations, including seizure onset, offset, and seizure type. Using this data, we constructed a deep neural network based on the time-series ResNet architecture, which could discriminate among seizure and non-seizure events. Our proposed approach achieved a seizure detection sensitivity of 95.65% and an overall false alarm rate of 0.15/24 hours during the evaluation, which spanned 5576 hours of total recording. Additionally, it resulted in an area under the receiver operating characteristic curve (AUC-ROC) of 0.98 and an area under the precision-re call curve (AUC-PRC) of 0.67 when averaged over 20 patients who experienced 46 convulsive seizures. These promising results suggest that the proposed seizure detection system can be effectively used for long-term ambulatory seizure monitoring. Future steps include validating our findings in larger datasets and assessing the utility of detection for additional seizure types.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.05919 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:24:06.088Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).