基于滞后庞加莱图提取的特征进行癫痫发作预测。
Epileptic seizure prediction based on features extracted from lagged Poincaré plots.
机构信息
School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
出版信息
Int J Neurosci. 2024 Apr;134(4):381-397. doi: 10.1080/00207454.2022.2106435. Epub 2022 Sep 26.
OBJECTIVE
The present work proposes a new epileptic seizure prediction method based on lagged Poincaré plot analysis of heart rate (HR).
METHODS
In this article, the Poincaré plots with six different lags (1-6) were constructed for four episodes of heart rate variability (HRV) before the seizures. Moreover, two features were extracted based on lagged Poincare plots, which include the angle between the time series and the ellipse density fitted to the RR points.
RESULTS
The proposed method was applied to 16 epileptic patients with 170 seizures. The results included sensitivity of 80.42% for the angle feature and 75.19% for the density feature. The false-positive rate was 0.15/Hr, which indicates that the system has superiority over the random predictor.
CONCLUSION
The proposed HRV-based epileptic seizure prediction method has the potential to be used in daily life because HR can be measured easily by using a wearable sensor.
目的
本研究提出了一种新的基于心率（HR）滞后 Poincaré 图分析的癫痫发作预测方法。
方法
本文构建了六个不同滞后（1-6）的 Poincaré 图，用于在癫痫发作前四个心率变异性（HRV）的片段。此外，基于滞后 Poincaré 图提取了两个特征，包括时间序列与拟合 RR 点的椭圆密度之间的夹角。
结果
该方法应用于 16 名癫痫患者的 170 次癫痫发作。结果包括角度特征的敏感性为 80.42%，密度特征的敏感性为 75.19%。假阳性率为 0.15/Hr，表明该系统优于随机预测器。
结论
所提出的基于 HRV 的癫痫发作预测方法具有在日常生活中应用的潜力，因为可以使用可穿戴传感器轻松测量 HR。
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