A Time-Series Scale Mixture Model of EEG with a Hidden Markov Structure for Epileptic Seizure Detection
A Time-Series Scale Mixture Model of EEG with a Hidden Markov Structure for Epileptic Seizure Detection
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DOI：
10.1109/embc46164.2021.9630840
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
A. Furui;T. Akiyama;T. Tsuji
中科院分区：
文献类型：
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作者：
A. Furui;T. Akiyama;T. Tsuji
本文提出了一种基于马尔可夫转换的尺度混合分布的时间序列随机模型来检测脑电图中的癫痫发作。在该模型中，每个时间点的脑电信号被假设为服从高斯分布的随机变量。高斯分布的协方差矩阵是用一个潜标度参数加权的，潜标度参数也是一个随机变量，导致协方差的随机波动。在这种随机关系的背景下，通过引入具有马尔可夫链的潜伏状态变量，可以根据癫痫发作状态来表示潜伏尺度参数分布的时间序列变化。在实验中，我们使用从临床数据集分解的多个频带的脑电图来评估所提出的癫痫发作检测模型的性能。结果表明，该模型能够以较高的灵敏度检测癫痫发作，并且优于几种基线。
In this paper, we propose a time-series stochastic model based on a scale mixture distribution with Markov transitions to detect epileptic seizures in electroencephalography (EEG). In the proposed model, an EEG signal at each time point is assumed to be a random variable following a Gaussian distribution. The covariance matrix of the Gaussian distribution is weighted with a latent scale parameter, which is also a random variable, resulting in the stochastic fluctuations of covariances. By introducing a latent state variable with a Markov chain in the background of this stochastic relationship, time-series changes in the distribution of latent scale parameters can be represented according to the state of epileptic seizures. In an experiment, we evaluated the performance of the proposed model for seizure detection using EEGs with multiple frequency bands decomposed from a clinical dataset. The results demonstrated that the proposed model can detect seizures with high sensitivity and outperformed several baselines.