Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland.
Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.
Epilepsia. 2020 Nov;61 Suppl 1(Suppl 1):S47-S54. doi: 10.1111/epi.16538. Epub 2020 Jun 2.
Reliably detecting focal seizures without secondary generalization during daily life activities, chronically, using convenient portable or wearable devices, would offer patients with active epilepsy a number of potential benefits, such as providing more reliable seizure count to optimize treatment and seizure forecasting, and triggering alarms to promote safeguarding interventions. However, no generic solution is currently available to reach these objectives. A number of biosignals are sensitive to specific forms of focal seizures, in particular heart rate and its variability for seizures affecting the neurovegetative system, and accelerometry for those responsible for prominent motor activity. However, most studies demonstrate high rates of false detection or poor sensitivity, with only a minority of patients benefiting from acceptable levels of accuracy. To tackle this challenging issue, several lines of technological progress are envisioned, including multimodal biosensing with cross-modal analytics, a combination of embedded and distributed self-aware machine learning, and ultra-low-power design to enable appropriate autonomy of such sophisticated portable solutions.
在日常生活活动中,使用方便的便携式或可穿戴设备可靠地检测无继发性泛化的局灶性发作,将为活动性癫痫患者带来诸多潜在益处,例如提供更可靠的发作次数以优化治疗和发作预测,并触发警报以促进保护干预。然而,目前尚无通用解决方案可以实现这些目标。许多生物信号对特定形式的局灶性发作敏感,特别是对影响自主神经系统的发作的心率及其变异性,以及对导致明显运动活动的发作的加速度计敏感。然而,大多数研究表明,假阳性检测率或敏感性较差,只有少数患者受益于可接受的准确度水平。为了解决这个具有挑战性的问题,人们设想了几种技术进步路线,包括多模态生物传感与跨模态分析、嵌入式和分布式自感知机器学习的结合,以及超低功耗设计,以实现这种复杂的便携式解决方案的适当自主性。