IEEE Trans Neural Syst Rehabil Eng. 2021;29:2037-2045. doi: 10.1109/TNSRE.2021.3113888. Epub 2021 Oct 8.
Real-time continuous tracking of seizure state is necessary to develop feedback neuromodulation therapy that can prevent or terminate a seizure early. Due to its high temporal resolution, high scalp coverage, and non-invasive applicability, electroencephalography (EEG) is a good candidate for seizure tracking. In this research, we make multiple seizure state estimations using a mixed-filter and multiple channels found over the entire sensor space; then by applying a Kalman filter, we produce a single seizure state estimation made up of these individual estimations. Using a modified wrapper feature selection, we determine two optimal features of mixed data type, one continuous and one binary analyzing all available channels. These features are used in a state-space framework to model the continuous hidden seizure state. Expectation maximization is performed offline on the training and validation data sets to estimate unknown parameters. The seizure state estimation process is performed for multiple channels, and the seizure state estimation is derived using a square-root Kalman filter. A second expectation maximization step is utilized to estimate the unknown square-root Kalman filter parameters. This method is tested in a real-time applicable way for seizure state estimation. Applying this approach, we obtain a single seizure state estimation with quantitative information about the likelihood of a seizure occurring, which we call seizure probability. Our results on the experimental data (CHB-MIT EEG database) validate the proposed estimation method and we achieve an average accuracy, sensitivity, and specificity of 92.7%, 92.8%, and 93.4%, respectively. The potential applications of this seizure estimation model are for closed-loop neuromodulation and long-term quantitative analysis of seizure treatment efficacy.
实时连续跟踪癫痫状态对于开发能够早期预防或终止癫痫发作的反馈神经调节治疗是必要的。由于其具有高时间分辨率、高头皮覆盖率和非侵入性适用性，脑电图（EEG）是癫痫跟踪的一个很好的候选者。在这项研究中，我们使用混合滤波器和在整个传感器空间上找到的多个通道进行多次癫痫状态估计；然后通过应用卡尔曼滤波器，我们生成由这些单独估计组成的单个癫痫状态估计。使用修改后的包装器特征选择，我们确定了两种混合数据类型的最优特征，一种是连续的，另一种是二进制的，分析了所有可用的通道。这些特征用于状态空间框架来模拟连续的隐藏癫痫状态。期望最大化在训练和验证数据集上离线执行，以估计未知参数。癫痫状态估计过程在多个通道上进行，并且使用平方根卡尔曼滤波器进行癫痫状态估计。利用第二个期望最大化步骤来估计未知的平方根卡尔曼滤波器参数。该方法以实时适用的方式用于癫痫状态估计的测试。应用这种方法，我们获得了单个癫痫状态估计，其中包含关于癫痫发作发生可能性的定量信息，我们称之为癫痫发作概率。我们在实验数据（CHB-MIT EEG 数据库）上的结果验证了所提出的估计方法，我们分别实现了 92.7%、92.8%和 93.4%的平均准确性、敏感性和特异性。这种癫痫估计模型的潜在应用是用于闭环神经调节和癫痫治疗效果的长期定量分析。
