Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Epilepsia. 2024 Nov;65(11):3255-3264. doi: 10.1111/epi.18109. Epub 2024 Sep 17.
This study aimed to assess whether population-level patterns in seizure occurrence previously observed in self-reported diaries, medical records, and electroencephalographic recordings were also present in tonic-clonic seizure (TCS) diaries produced via the combined input of a US Food and Drug Administration-cleared wristband with an artificial intelligence detection algorithm and patient self-reports. We also investigated the characteristics of patient interactions with wearable seizure alerts.
We analyzed wristband data from patients with TCSs who had at least three reported TCSs over a minimum of 90 days. We quantified TCS frequency and cycles, and the relationship between the mean and variability of monthly TCS counts. We also assessed interaction metrics such as false alarm dismissal and seizure confirmation rates.
Applying strict criteria for usable data, we reviewed 137 490 TCSs from 3012 patients, with a median length of TCS alert records of 445 days (range = 90-1806). Analyses showed consistency between prior diary studies and the present data concerning (1) the distribution of monthly TCS frequency (median = 3.1, range = .08-26); (2) the linear relationship (slope = .79, R = .83) between the logarithm of the mean and the logarithm of the SD of monthly TCS frequency (L-relationship); and (iii) the prevalence of multiple coexisting seizure cycles, including circadian (84.0%), weekly (24.6%), and long-term cycles (31.1%).
Key population-level patterns in seizure occurrence are recapitulated in wrist-worn device recordings, supporting their validity for tracking TCS burden. Compared to other approaches, wearables can provide noninvasive, objective, long-term data, revealing cycles in seizure risk. However, improved patient engagement with wristband alerts and further validation of detection accuracy in ambulatory settings are needed. Together, these findings suggest that data from smart wristbands may be used to derive features of TCS records and, ultimately, facilitate remote monitoring and the development of personalized forecasting tools for TCS management. Our findings may not generalize to other types of seizures.
本研究旨在评估先前在自我报告日记、医疗记录和脑电图记录中观察到的人群中发作发生模式是否也存在于通过美国食品和药物管理局批准的腕带与人工智能检测算法和患者自我报告相结合产生的强直阵挛发作 (TCS) 日记中。我们还研究了患者与可穿戴式发作警报互动的特征。
我们分析了至少有 3 次报告的 TCS 发作且至少有 90 天记录的 TCS 患者的腕带数据。我们量化了 TCS 发作频率和周期，以及每月 TCS 计数的平均值和变异性之间的关系。我们还评估了交互指标，如误报排除率和发作确认率。
应用严格的数据可用性标准，我们回顾了 3012 名患者的 137490 次 TCS，TCS 警报记录的中位数长度为 445 天（范围 90-1806 天）。分析显示，先前的日记研究与本研究数据之间存在一致性，涉及（1）每月 TCS 发作频率的分布（中位数=3.1，范围=0.08-26）；（2）每月 TCS 发作频率的对数与标准差对数之间的线性关系（斜率=0.79，R=0.83）（L 关系）；以及（3）多个共存的发作周期的普遍性，包括昼夜节律（84.0%）、每周（24.6%）和长期周期（31.1%）。
发作发生的关键人群模式在腕戴式设备记录中重现，支持其用于跟踪 TCS 负担的有效性。与其他方法相比，可穿戴设备可以提供非侵入性、客观、长期的数据，揭示发作风险的周期性。然而，需要改善患者与腕带警报的互动，并在非卧床环境中进一步验证检测准确性。总的来说，这些发现表明，智能腕带的数据可用于提取 TCS 记录的特征，并最终促进 TCS 管理的远程监测和个性化预测工具的开发。我们的发现可能不适用于其他类型的发作。
