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癫痫惊厥减缓的自动视频检测作为癫痫发作后神经元崩溃的先兆

Automated Video Detection of Epileptic Convulsion Slowing as a Precursor for Post-Seizure Neuronal Collapse.

作者信息

Kalitzin Stiliyan N, Bauer Prisca R, Lamberts Robert J, Velis Demetrios N, Thijs Roland D, Lopes Da Silva Fernando H

机构信息

* Foundation Epilepsy Institutes Netherlands (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands.

† Image Sciences Institute, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands.

出版信息

Int J Neural Syst. 2016 Dec;26(8):1650027. doi: 10.1142/S0129065716500271. Epub 2016 Apr 4.

Abstract

Automated monitoring and alerting for adverse events in people with epilepsy can provide higher security and quality of life for those who suffer from this debilitating condition. Recently, we found a relation between clonic slowing at the end of a convulsive seizure (CS) and the occurrence and duration of a subsequent period of postictal generalized EEG suppression (PGES). Prolonged periods of PGES can be predicted by the amount of progressive increase of interclonic intervals (ICIs) during the seizure. The purpose of the present study is to develop an automated, remote video sensing-based algorithm for real-time detection of significant clonic slowing that can be used to alert for PGES. This may help preventing sudden unexpected death in epilepsy (SUDEP). The technique is based on our previously published optical flow video sequence processing paradigm that was applied for automated detection of major motor seizures. Here, we introduce an integral Radon-like transformation on the time-frequency wavelet spectrum to detect log-linear frequency changes during the seizure. We validate the automated detection and quantification of the ICI increase by comparison to the results from manually processed electroencephalography (EEG) traces as "gold standard". We studied 48 cases of convulsive seizures for which synchronized EEG-video recordings were available. In most cases, the spectral ridges obtained from Gabor-wavelet transformations of the optical flow group velocities were in close proximity to the ICI traces detected manually from EEG data during the seizure. The quantification of the slowing-down effect measured by the dominant angle in the Radon transformed spectrum was significantly correlated with the exponential ICI increase factors obtained from manual detection. If this effect is validated as a reliable precursor of PGES periods that lead to or increase the probability of SUDEP, the proposed method would provide an efficient alerting device.

摘要

对癫痫患者的不良事件进行自动监测和警报可为患有这种使人衰弱疾病的患者提供更高的安全性和生活质量。最近,我们发现惊厥性癫痫发作(CS)末期的阵挛性减慢与随后的发作后广泛性脑电图抑制(PGES)期的发生和持续时间之间存在关联。PGES的延长可通过癫痫发作期间阵挛间期(ICI)的逐渐增加量来预测。本研究的目的是开发一种基于远程视频传感的自动化算法,用于实时检测显著的阵挛性减慢,以用于对PGES发出警报。这可能有助于预防癫痫猝死(SUDEP)。该技术基于我们之前发表的光流视频序列处理范式,该范式用于自动检测主要运动性癫痫发作。在此,我们在时频小波频谱上引入一种积分拉东样变换,以检测癫痫发作期间的对数线性频率变化。我们通过与作为“金标准”的手动处理脑电图(EEG)痕迹的结果进行比较,验证了ICI增加的自动检测和量化。我们研究了48例有同步EEG-视频记录的惊厥性癫痫发作病例。在大多数情况下,从光流群速度的加博尔小波变换获得的频谱脊与癫痫发作期间从EEG数据手动检测到的ICI痕迹非常接近。通过拉东变换频谱中的主导角度测量的减慢效应的量化与从手动检测获得的指数ICI增加因子显著相关。如果这种效应被验证为导致或增加SUDEP可能性的PGES期的可靠先兆,那么所提出的方法将提供一种有效的警报装置。

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