Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Minnesota, Rochester, USA.
School of Engineering, University of North Florida, Florida, Jacksonville, USA.
Epilepsia. 2023 Jun;64(6):1627-1639. doi: 10.1111/epi.17607. Epub 2023 Apr 20.
The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing.
In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist-worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter-Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology.
Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal.
Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time-varying approaches to epilepsy care.
影响癫痫发作时间的因素尚未完全阐明，癫痫发作的不可预测性仍是导致残疾的主要原因。节律生物学的研究表明，周期性生理现象普遍存在，免疫、内分泌、代谢、神经和心血管功能均存在每日和多日周期。此外，对慢性脑记录的研究表明，癫痫发作的风险与大脑活动的每日和多日周期有关。在这里，我们首次描述了一系列生理信号、大脑活动和癫痫发作时间之间的周期性调节关系。
在这项队列研究中，14 名受试者佩戴了一种多模式腕戴式传感器（记录心率、加速度计、皮肤电活动和温度）和植入式反应性神经刺激系统（记录间发性癫痫样异常和脑电图癫痫发作），进行了慢性动态监测。小波和滤波器-希尔伯特频谱分析描述了大脑和可穿戴设备记录中的昼夜节律和多日周期。循环统计评估了脑电图癫痫发作的时间和生理周期。
10 名受试者符合纳入标准。平均记录时间为 232 天。7 名受试者有可靠的脑电图癫痫发作检测（平均 76 次癫痫发作）。所有受试者的所有可穿戴设备信号均存在多日周期。在 5 名（温度）、4 名（心率、阵发性皮肤电活动）和 3 名（加速度计、心率变异性、紧张性皮肤电活动）受试者中，癫痫发作时间与多日周期同步锁定。值得注意的是，在从心率中回归行为协变量后，7 名受试者中有 6 名的心率信号与癫痫发作相位锁定。
癫痫发作时间与多个生理过程的每日和多日周期相关。慢性多模态可穿戴设备记录可以将罕见的阵发性事件（如癫痫发作）置于个体更广泛的节律生物学背景下。可穿戴设备可以促进对影响癫痫发作风险的因素的理解，并实现针对癫痫的个性化时变治疗方法。
