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.
翻译:在本文中,我们提出一个时间序列随机模型,基于与Markov相交的混合物比例分布,以检测电脑造影中的癫痫发作(EEG)。在拟议模型中,每个时间点的 EEG 信号被假定为高山分布后的一个随机变量。高山分布的共变量矩阵与潜伏比例参数加权,这也是一个随机变量,导致常态的随机波动。通过引入一个潜伏状态变量,在这一随机关系背景下带Markov链,潜伏比例参数分布的时间序列变化可以按照癫痫发作状态来表示。在一次实验中,我们用多频带从临床数据集分解的 EEG 评估了拟议的缉获检测模型的性能。结果显示,拟议的模型能够以高敏感度和优于多个基线的方式检测缉获。