Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, 3010 Bern, Switzerland.
Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, California 94143, USA.
Chaos. 2021 Jan;31(1):013138. doi: 10.1063/5.0026733.
Paroxysms are sudden, unpredictable, short-lived events that abound in physiological processes and pathological disorders, from cellular functions (e.g., hormone secretion and neuronal firing) to life-threatening attacks (e.g., cardiac arrhythmia, epileptic seizures, and diabetic ketoacidosis). With the increasing use of personal chronic monitoring (e.g., electrocardiography, electroencephalography, and glucose monitors), the discovery of cycles in health and disease, and the emerging possibility of forecasting paroxysms, the need for suitable methods to evaluate synchrony-or phase-clustering-between events and related underlying physiological fluctuations is pressing. Here, based on examples in epilepsy, where seizures occur preferentially in certain brain states, we characterize different methods that evaluate synchrony in a controlled timeseries simulation framework. First, we compare two methods for extracting the phase of event occurrence and deriving the phase-locking value, a measure of synchrony: (M1) fitting cycles of fixed period-length vs (M2) deriving continuous cycles from a biomarker. In our simulations, M2 provides stronger evidence for cycles. Second, by systematically testing the sensitivity of both methods to non-stationarity in the underlying cycle, we show that M2 is more robust. Third, we characterize errors in circular statistics applied to timeseries with different degrees of temporal clustering and tested with different strategies: Rayleigh test, Poisson simulations, and surrogate timeseries. Using epilepsy data from 21 human subjects, we show the superiority of testing against surrogate time-series to minimize false positives and false negatives, especially when used in combination with M1. In conclusion, we show that only time frequency analysis of continuous recordings of a related bio-marker reveals the full extent of cyclical behavior in events. Identifying and forecasting cycles in biomedical timeseries will benefit from recordings using emerging wearable and implantable devices, so long as conclusions are based on conservative statistical testing.
发作是生理过程和病理紊乱中突然、不可预测、短暂的事件，从细胞功能（例如激素分泌和神经元放电）到危及生命的发作（例如心律失常、癫痫发作和糖尿病酮症酸中毒）。随着个人慢性监测（例如心电图、脑电图和血糖仪）的使用增加、健康和疾病中周期的发现以及发作预测的可能性出现，评估事件之间以及相关潜在生理波动的同步性或相位聚类的合适方法的需求变得紧迫。在这里，基于癫痫发作中发作优先发生在某些脑状态的例子，我们在受控时间序列模拟框架中描述了评估同步性的不同方法。首先，我们比较了两种用于提取事件发生相位和得出相位锁定值（衡量同步性的指标）的方法：（M1）拟合固定周期长度的周期与（M2）从生物标志物中得出连续周期。在我们的模拟中，M2 提供了更强的证据表明存在周期。其次，通过系统地测试这两种方法对基础周期非平稳性的敏感性，我们表明 M2 更稳健。第三，我们描述了应用于具有不同时间聚类程度的时间序列的循环统计中的误差，并使用不同策略进行了测试：瑞利检验、泊松模拟和替代时间序列。使用来自 21 名人类受试者的癫痫数据，我们表明，使用替代时间序列进行测试可以最大程度地减少假阳性和假阴性，尤其是与 M1 结合使用时。总之，我们表明，只有对相关生物标志物的连续记录进行时频分析才能揭示事件中循环行为的全部范围。在生物医学时间序列中识别和预测周期将受益于新兴的可穿戴和可植入设备的记录，只要结论基于保守的统计检验。
