We propose a generative model for single-channel EEG that incorporates the constraints experts actively enforce during visual scoring. The framework takes the form of a dynamic Bayesian network with depth in both the latent variables and the observation likelihoods-while the hidden variables control the durations, state transitions, and robustness, the observation architectures parameterize Normal-Gamma distributions. The resulting model allows for time series segmentation into local, reoccurring dynamical regimes by exploiting probabilistic models and deep learning. Unlike typical detectors, our model takes the raw data (up to resampling) without pre-processing (e.g., filtering, windowing, thresholding) or post-processing (e.g., event merging). This not only makes the model appealing to real-time applications, but it also yields interpretable hyperparameters that are analogous to known clinical criteria. We derive algorithms for exact, tractable inference as a special case of Generalized Expectation Maximization via dynamic programming and backpropagation. We validate the model on three public datasets and provide support that more complex models are able to surpass state-of-the-art detectors while being transparent, auditable, and generalizable.
翻译:我们提出单一通道EEG的基因化模型,该模型包含专家在视觉评分期间积极执行的限制因素。框架的形式是动态贝叶西亚网络,其深度包括潜伏变量和观察可能性,而隐藏变量控制时间长度、状态转型和稳健度,观测结构结构参数将正常-伽马分布参数化。由此形成的模型允许利用概率模型和深层次学习,将时间序列分割成局部的、重复的动态系统。与典型的探测器不同,我们的模型采用原始数据(直到重新取样),而不进行预处理(例如过滤、窗口、阈值)或后处理(例如事件合并)。这不仅使模型吸引实时应用,而且还产生可解释的超参数,类似于已知的临床标准。我们从精确、可移植的推论中得出精确的算法,作为通过动态编程和反演算法实现普遍期望最大化的特殊案例。我们验证了三个公共数据集的模型,并为更复杂的模型提供支持,这些模型既可以超越州级的、透明、可控和可操作的通用的检测器。