Liu Xiang, Wang Juan, Shang Junliang, Liu Jinxing, Dai Lingyun, Yuan Shasha
School of Computer Science, Qufu Normal University, Rizhao 276826, China.
Brain Sci. 2022 Sep 22;12(10):1275. doi: 10.3390/brainsci12101275.
Electroencephalography (EEG) records the electrical activity of the brain, which is an important tool for the automatic detection of epileptic seizures. It is certainly a very heavy burden to only recognize EEG epilepsy manually, so the method of computer-assisted treatment is of great importance. This paper presents a seizure detection algorithm based on variational modal decomposition (VMD) and a deep forest (DF) model. Variational modal decomposition is performed on EEG recordings, and the first three variational modal functions (VMFs) are selected to construct the time-frequency distribution of the EEG signals. Then, the log-Euclidean covariance matrix (LECM) is computed to represent the EEG properties and form EEG features. The deep forest model is applied to complete the EEG signal classification, which is a non-neural network deep model with a cascade structure that performs feature learning through the forest. In addition, to improve the classification accuracy, postprocessing techniques are performed to generate the discriminant results by moving average filtering and adaptive collar expansion. The algorithm was evaluated on the Bonn EEG dataset and the Freiburg long-term EEG dataset, and the former achieved a sensitivity and specificity of 99.32% and 99.31%, respectively. The mean sensitivity and specificity of this method for the 21 patients in the Freiburg dataset were 95.2% and 98.56%, respectively, with a false detection rate of 0.36/h. These results demonstrate the superior performance advantage of our algorithm and indicate its great research potential in epilepsy detection.
脑电图（EEG）记录大脑的电活动，它是自动检测癫痫发作的重要工具。仅靠人工识别脑电图癫痫无疑是一项非常沉重的负担，因此计算机辅助治疗方法至关重要。本文提出了一种基于变分模态分解（VMD）和深度森林（DF）模型的癫痫发作检测算法。对脑电图记录进行变分模态分解，选择前三个变分模态函数（VMF）来构建脑电信号的时频分布。然后，计算对数欧几里得协方差矩阵（LECM）以表示脑电图特性并形成脑电图特征。应用深度森林模型完成脑电信号分类，它是一种具有级联结构的非神经网络深度模型，通过森林进行特征学习。此外，为了提高分类准确率，采用后处理技术通过移动平均滤波和自适应阈值扩展来生成判别结果。该算法在波恩脑电数据集和弗莱堡长期脑电数据集上进行了评估，前者的灵敏度和特异性分别达到了99.32%和99.31%。该方法对弗莱堡数据集中21例患者的平均灵敏度和特异性分别为95.2%和98.56%，误检率为0.36/小时。这些结果证明了我们算法的优越性能优势，并表明其在癫痫检测方面具有巨大的研究潜力。
