Gagliano L, Assi E Bou, Toffa D H, Nguyen D K, Sawan M
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:698-701. doi: 10.1109/EMBC44109.2020.9175739.
Over a third of patients suffering from epilepsy continue to live with recurrent disabling seizures and would greatly benefit from personalized seizure forecasting. While electroencephalography (EEG) remains most popular for studying subject-specific epileptic precursors, dysfunctions of the autonomous nervous system, notably cardiac activity measured in heart rate variability (HRV), have also been associated with epileptic seizures. This work proposes an unsupervised clustering technique which aims to automatically identify preictal HRV changes in 9 patients who underwent simultaneous electrocardiography (ECG) and intracranial EEG presurgical monitoring at the University of Montreal Hospital Center. A 2-class k-means clustering combined with a quantitative preictal HRV change detection technique were adopted in a subject- and seizure-specific manner. Results indicate inter and intra-patient variability in preictal HRV changes (between 3.5 and 6.5 min before seizure onset) and a statistically significant negative correlation between the time of change in HRV state and the duration of seizures (p<0.05). The presented findings show promise for new avenues of research regarding multimodal seizure prediction and unsupervised preictal time assessment.Clinical Relevance- This study proposed an unsupervised technique for quantitatively identifying preictal HRV changes which can be eventually used to implement an ECG-based seizure forecasting algorithm.
超过三分之一的癫痫患者仍饱受反复发作的致残性癫痫之苦，个性化癫痫发作预测将使他们受益匪浅。虽然脑电图（EEG）仍是研究个体特异性癫痫发作先兆最常用的方法，但自主神经系统功能障碍，尤其是通过心率变异性（HRV）测量的心脏活动，也与癫痫发作有关。这项研究提出了一种无监督聚类技术，旨在自动识别9名在蒙特利尔大学医院中心同时接受心电图（ECG）和颅内EEG术前监测的患者发作前的HRV变化。采用了一种2类k均值聚类方法，并结合一种定量的发作前HRV变化检测技术，该方法以个体和发作特异性的方式进行。结果表明，发作前HRV变化（发作开始前3.5至6.5分钟之间）存在患者间和患者内变异性，HRV状态变化时间与发作持续时间之间存在统计学显著负相关（p<0.05）。研究结果为多模态癫痫发作预测和无监督发作前时间评估的新研究途径带来了希望。临床意义——本研究提出了一种无监督技术，用于定量识别发作前HRV变化，最终可用于实施基于ECG的癫痫发作预测算法。
