University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal.
Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milan, Italy.
Sci Rep. 2021 Mar 16;11(1):5987. doi: 10.1038/s41598-021-85350-y.
Electrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes the seizure's clinical manifestation. The preictal interval has not yet been clinically parametrized. Furthermore, the duration of this interval varies for seizures both among patients and from the same patient. In this study, we performed a heart rate variability (HRV) analysis to investigate the discriminative power of the features of HRV in the identification of the preictal interval. HRV information extracted from the linear time and frequency domains as well as from nonlinear dynamics were analysed. We inspected data from 238 temporal lobe seizures recorded from 41 patients with drug-resistant epilepsy from the EPILEPSIAE database. Unsupervised methods were applied to the HRV feature dataset, thus leading to a new perspective in preictal interval characterization. Distinguishable preictal behaviour was exhibited by 41% of the seizures and 90% of the patients. Half of the preictal intervals were identified in the 40 min before seizure onset. The results demonstrate the potential of applying clustering methods to HRV features to deepen the current understanding of the preictal state.
在寻找癫痫发作前存在间隔的证据的过程中，研究了持续数小时的心电图（ECG）记录，该间隔在正常 ECG 轨迹之后并先于发作的临床表现。目前尚未对发作前间隔进行临床参数化。此外，该间隔的持续时间在患者之间和同一患者的癫痫发作之间都有所不同。在这项研究中，我们进行了心率变异性（HRV）分析，以研究 HRV 特征在识别发作前间隔中的区分能力。分析了从线性时域和频域以及非线性动力学中提取的 HRV 信息。我们检查了来自 41 名耐药性癫痫患者的 238 例颞叶癫痫发作的 EPILEPSIAE 数据库中的数据。将无监督方法应用于 HRV 特征数据集，从而为发作前间隔特征提供了新的视角。41%的癫痫发作和 90%的患者表现出可区分的发作前行为。一半的发作前间隔在发作前 40 分钟内被识别。结果表明，应用聚类方法对 HRV 特征进行分析，有助于加深对发作前状态的理解。
