School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
Phys Eng Sci Med. 2024 Mar;47(1):31-47. doi: 10.1007/s13246-023-01340-6. Epub 2023 Sep 25.
Although it is clinically important, a reliable and economical solution to automatic seizure detection for patients at home is yet to be developed. Traditional algorithms rely on multi-channel EEG signals and features of canonical EEG power description. This study is aimed to propose an effective single-channel EEG seizure detection method centered on novel EEG power parameterization and channel selection algorithms. We employed the publicly available multi-channel CHB-MIT Scalp EEG database to gauge the effectiveness of our approach. We first adapted a power spectra parameterization algorithm to characterize the aperiodic and periodic components of the ictal and inter-ictal EEGs. We selected four features based on their statistical significance and interpretability, and developed a ranking approach to channel selection for each patient. We then tested the effectiveness of our approaches to channel and feature selection for automatic seizure detection using support vector machine (SVM) as the classifier. The performance of our algorithm was evaluated using five-fold cross-validation and compared to those methods of comparable complexity (using one or two channels of EEG), in terms of accuracy, specificity, sensitivity, precision and F1 score. Some channels of EEG signals show strikingly different distributions of PSD features between the ictal and inter-ictal states. Four features including the offset and exponent parameters for the aperiodic component and the first and second highest total power (TPW1 and TPW2) form the basis of channel selection and the input of SVM classifier. The selected channel is found to be patient-specific. Our approach has achieved a mean sensitivity of 95.6%, specificity of 99.2%, accuracy of 98.6%, precision of 95.5%, and F1 score of 95.5%. Compared with algorithms in previous studies that used one or two channels of EEG signals, ours outperforms in specificity and accuracy with comparable sensitivity. EEG power spectra parameterization to feature extraction and feature ranking-based channel selection are found to enable efficient and effective automatic seizure detection based on single-channel EEG signal.
尽管临床意义重大，但尚未开发出一种可靠且经济的家用自动癫痫检测解决方案。传统算法依赖于多通道 EEG 信号和典型 EEG 功率描述特征。本研究旨在提出一种有效的单通道 EEG 癫痫检测方法，该方法以新的 EEG 功率参数化和通道选择算法为中心。我们使用了公开的多通道 CHB-MIT 头皮 EEG 数据库来评估我们方法的有效性。我们首先采用了一种功率谱参数化算法来描述癫痫发作期和发作间期 EEG 的非周期性和周期性成分。我们根据其统计显著性和可解释性选择了四个特征，并为每个患者开发了一种通道选择排序方法。然后，我们使用支持向量机（SVM）作为分类器，测试了我们的通道和特征选择方法在自动癫痫检测中的有效性。我们的算法使用五重交叉验证进行评估，并与具有可比性（使用 EEG 的一个或两个通道）的方法进行比较，在准确性、特异性、敏感性、精度和 F1 分数方面进行比较。一些 EEG 信号通道的 PSD 特征在癫痫发作期和发作间期之间显示出明显不同的分布。四个特征包括非周期性成分的偏移和指数参数以及第一和第二总功率（TPW1 和 TPW2），它们构成了通道选择和 SVM 分类器输入的基础。选择的通道是特定于患者的。我们的方法实现了 95.6%的平均敏感性、99.2%的特异性、98.6%的准确性、95.5%的精度和 95.5%的 F1 分数。与之前使用一个或两个 EEG 信号通道的算法相比，我们的算法在特异性和准确性方面表现更好，敏感性相当。EEG 功率谱参数化用于特征提取和基于特征排序的通道选择，被发现可以有效地实现基于单通道 EEG 信号的自动癫痫检测。
