A Bayesian framework for analyzing iEEG data from a rat model of epilepsy.
A Bayesian framework for analyzing iEEG data from a rat model of epilepsy.
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用于分析癫痫大鼠模型的 iEEG 数据的贝叶斯框架。
DOI：
10.1109/iembs.2011.6090355
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Sarma,SrideviV
中科院分区：
文献类型：
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作者：
Santaniello,Sabato;Sherman,DavidL;Mirski,MarekA;Thakor,NitishV;Sarma,SrideviV
癫痫发作的早期检测需要从多变量数据中计算相关统计数据，并根据这些统计数据定义一个鲁棒的决策策略，该策略准确地检测从正常到发作期（有问题）状态的转变。我们将患病的大脑建模为具有两个隐藏的临床状态（正常和发作期）的隐马尔可夫模型（HMM）。HMM的输出是从多变量神经测量计算的统计量。贝叶斯框架被开发来分析在当前和过去输出测量的情况下处于发作期状态的后验条件概率。我们将这种方法应用于癫痫大鼠模型中丘脑-皮层发作通路的多通道皮层内脑电图（iEEG）。我们首先定义输出统计量为用谱技术计算的EEG通道上的连接矩阵的最大奇异值，然后，我们从该统计量估计HMM转移概率，并跟踪处于发作期状态的后验概率（“信息状态变量”）。我们展示了信息状态变量如何随时间变化，当这个变量大于0.5时，我们预测癫痫发作。这种贝叶斯策略显着改善了机会水平和基于阈值的预测器。
The early detection of epileptic seizures requires computing relevant statistics from multivariate data and defining a robust decision strategy as a function of these statistics that accurately detects the transition from the normal to the peri-ictal (problematic) state. We model the afflicted brain as a hidden Markov model (HMM) with two hidden clinical states (normal and peri-ictal). The output of the HMM is a statistic computed from multivariate neural measurements. A Bayesian framework is developed to analyze the a posteriori conditional probability of being in peri-ictal state given current and past output measurements. We apply this method to multichannel intracortical EEGs (iEEGs) from the thalamo-cortical ictal pathway in an epilepsy rat model. We first define the output statistic as the max singular value of a connectivity matrix computed on the EEG channels with spectral techniques Then, we estimate the HMM transition probabilities from this statistic and track the a posteriori probability of being in peri-ictal state (the “information state variable”). We show how the information state variable changes as a function of time and we predict a seizure when this variable becomes greater than 0.5. This Bayesian strategy significantly improves over chance level and heuristically-chosen threshold-based predictors.