目的:对影响癫痫发作时间的因素知之甚少,癫痫发作的不可预测性仍然是残疾的主要原因。时间生物学的工作表明,周期性的生理现象无处不在,每天和多天的免疫周期明显,内分泌,新陈代谢,神经学,和心血管功能。此外,慢性大脑记录的研究发现,癫痫发作的风险与大脑活动的每日和多日周期有关。这里,我们提供了一组不同生理信号的周期性调制之间的关系的第一个表征,大脑活动,和癫痫发作时间。
方法:在这项队列研究中,14名受试者接受了使用多模式腕部穿戴传感器的慢性动态监测(记录心率,加速计,皮肤电活动,和温度)和植入的反应性神经刺激系统(记录发作间癫痫样异常和电图癫痫发作)。小波和滤波器希尔伯特光谱分析表征了大脑和可穿戴记录中的昼夜节律和多天周期。循环统计评估了生理学中的电记录癫痫发作时间和周期。
结果:10名受试者符合纳入标准。平均记录持续时间为232天。七名受试者进行了可靠的脑电图癫痫发作检测(平均=76次癫痫发作)。在所有受试者的所有可穿戴设备信号中都存在多天周期。癫痫发作时间在五个(温度)中被锁定为多天周期,四(心率,阶段性皮肤电活动),和三个(加速度计,心率变异性,强直性皮肤电活动)受试者。值得注意的是,从心率回归行为协变量后,7名受试者中有6名癫痫发作相位锁定于残余心率信号。
结论:癫痫发作时间与多种生理过程中的每日和多日周期相关。慢性多模式可穿戴设备记录可以发生罕见的阵发性事件,比如癫痫发作,在更广泛的个体时间生物学背景下。可穿戴设备可以促进对影响癫痫发作风险的因素的理解,并实现个性化的时变方法来治疗癫痫。
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.