利用可穿戴设备预测癫痫发作：一种混合的短期和长期伪前瞻性方法。
Forecasting epileptic seizures with wearable devices: A hybrid short- and long-horizon pseudo-prospective approach.
作者信息
Nasseri Mona, Stirling Rachel E, Viana Pedro F, Cui Jie, Nurse Ewan, Karoly Philippa J, Kremen Vaclav, Dümpelmann Matthias, Worrell Gregory A, Freestone Dean R, Richardson Mark P, Brinkmann Benjamin H
机构信息
Departments of Neurology and Physiology and Biomedical Engineering, Mayo Foundation, Rochester, Minnesota, USA.
School of Engineering, University of North Florida, Jacksonville, Florida, USA.
出版信息
Epilepsia. 2025 May 24. doi: 10.1111/epi.18466.
OBJECTIVE
Seizure unpredictability can be debilitating and dangerous for people with epilepsy. Accurate seizure forecasters could improve quality of life for those with epilepsy but must be practical for long-term use. This study presents the first validation of a seizure-forecasting system using ultra-long-term, non-invasive wearable data.
METHODS
Eleven participants with epilepsy were recruited for continuous monitoring, capturing heart rate and step count via wrist-worn devices and seizures via electroencephalography (average recording duration of 337 days). Two hybrid models-combining machine learning and cycle-based methods-were proposed to forecast seizures at both short (minutes) and long (up to 44 days) horizons.
RESULTS
The Seizure Warning System (SWS), designed for forecasting near-term seizures, and the Seizure Risk System (SRS), designed for forecasting long-term risk, both outperformed traditional models. In addition, the SRS reduced high-risk time by 29% while increasing sensitivity by 11%.
SIGNIFICANCE
These improvements mark a significant advancement in making seizure forecasting more practical and effective.
目的
癫痫发作的不可预测性对癫痫患者来说可能是致残且危险的。准确的癫痫发作预测器可以改善癫痫患者的生活质量，但必须便于长期使用。本研究首次对使用超长期、非侵入性可穿戴数据的癫痫发作预测系统进行了验证。
方法
招募了11名癫痫患者进行连续监测，通过腕戴设备获取心率和步数，并通过脑电图记录癫痫发作情况（平均记录时长为337天）。提出了两种混合模型——结合机器学习和基于周期的方法——来预测短期（数分钟）和长期（长达44天）的癫痫发作。
结果
用于预测近期癫痫发作的癫痫预警系统（SWS）和用于预测长期风险的癫痫风险系统（SRS）均优于传统模型。此外，SRS将高风险时间减少了29%，同时将敏感性提高了11%。
意义
这些改进标志着在使癫痫发作预测更具实用性和有效性方面取得了重大进展。
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