Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, personalized treatments. Although recent work has convincingly demonstrated that seizure risk assessment is in principle possible, these early approaches relied largely on complex, often invasive setups including intracranial electrocorticography, implanted devices, and multichannel electroencephalography, and required patient‐specific adaptation or learning to perform optimally, all of which limit translation to broad clinical application. To facilitate broader adaptation of seizure forecasting in clinical practice, noninvasive, easily applicable techniques that reliably assess seizure risk without much prior tuning are crucial. Wristbands that continuously record physiological parameters, including electrodermal activity, body temperature, blood volume pulse, and actigraphy, may afford monitoring of autonomous nervous system function and movement relevant for such a task, hence minimizing potential complications associated with invasive monitoring and avoiding stigma associated with bulky external monitoring devices on the head.

中文翻译：

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从腕带传感器数据中进行机器学习，用于可穿戴、无创癫痫发作预测

癫痫发作预测可以为患者提供及时的警告，以适应他们的日常活动，并帮助临床医生提供更客观、更个性化的治疗。尽管最近的工作已经令人信服地证明原则上可以进行癫痫发作风险评估，但这些早期方法主要依赖于复杂的、通常是侵入性的设置，包括颅内皮层电图、植入装置和多通道脑电图，并且需要针对患者的特定适应或学习以最佳方式执行，所有其中限制了广泛的临床应用的翻译。为了在临床实践中更广泛地适应癫痫预测，无需大量预先调整即可可靠地评估癫痫风险的无创、易于应用的技术至关重要。连续记录生理参数的腕带，