School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia.
Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia; Seer Medical, Melbourne, Australia.
EBioMedicine. 2023 Jul;93:104656. doi: 10.1016/j.ebiom.2023.104656. Epub 2023 Jun 16.
Seizure risk forecasting could reduce injuries and even deaths in people with epilepsy. There is great interest in using non-invasive wearable devices to generate forecasts of seizure risk. Forecasts based on cycles of epileptic activity, seizure times or heart rate have provided promising forecasting results. This study validates a forecasting method using multimodal cycles recorded from wearable devices.
Seizure and heart rate cycles were extracted from 13 participants. The mean period of heart rate data from a smartwatch was 562 days, with a mean of 125 self-reported seizures from a smartphone app. The relationship between seizure onset time and phases of seizure and heart rate cycles was investigated. An additive regression model was used to project heart rate cycles. The results of forecasts using seizure cycles, heart rate cycles, and a combination of both were compared. Forecasting performance was evaluated in 6 of 13 participants in a prospective setting, using long-term data collected after algorithms were developed.
The results showed that the best forecasts achieved a mean area under the receiver-operating characteristic curve (AUC) of 0.73 for 9/13 participants showing performance above chance during retrospective validation. Subject-specific forecasts evaluated with prospective data showed a mean AUC of 0.77 with 4/6 participants showing performance above chance.
The results of this study demonstrate that cycles detected from multimodal data can be combined within a single, scalable seizure risk forecasting algorithm to provide robust performance. The presented forecasting method enabled seizure risk to be estimated for an arbitrary future period and could be generalised across a range of data types. In contrast to earlier work, the current study evaluated forecasts prospectively, in subjects blinded to their seizure risk outputs, representing a critical step towards clinical applications.
This study was funded by an Australian Government National Health & Medical Research Council and BioMedTech Horizons grant. The study also received support from the Epilepsy Foundation of America's 'My Seizure Gauge' grant.
癫痫患者的癫痫发作风险预测可降低伤害甚至死亡风险。人们对使用非侵入性可穿戴设备生成癫痫发作风险预测非常感兴趣。基于癫痫活动周期、发作时间或心率的预测已经提供了有前景的预测结果。本研究验证了一种使用可穿戴设备记录的多模态周期进行预测的方法。
从 13 名参与者中提取了癫痫发作和心率周期。智能手表心率数据的平均周期为 562 天，智能手机应用程序记录的平均 125 次自我报告癫痫发作。研究了癫痫发作开始时间与癫痫发作和心率周期相位之间的关系。使用加法回归模型预测心率周期。比较了使用癫痫发作周期、心率周期以及两者结合进行预测的结果。在开发算法后，使用前瞻性设置在 13 名参与者中的 6 名参与者的长期数据评估了预测性能。
结果表明，在回顾性验证中，最佳预测结果在 9/13 名参与者中达到了 0.73 的接收者操作特征曲线（AUC）均值，表现优于机会水平。使用前瞻性数据评估的个体预测显示，6 名参与者中有 4 名的 AUC 均值为 0.77，表现优于机会水平。
本研究结果表明，可从多模态数据中检测到的周期可在单个可扩展的癫痫发作风险预测算法中组合使用，以提供稳健的性能。所提出的预测方法能够估计任意未来时间段的癫痫发作风险，并可推广到一系列数据类型。与早期的工作相比，本研究在对其癫痫发作风险输出不知情的参与者中进行了前瞻性评估，这是迈向临床应用的关键一步。
本研究由澳大利亚政府国家健康与医学研究委员会和 BiomedTech Horizons 资助。该研究还得到了美国癫痫基金会“我的癫痫仪”赠款的支持。
