Department of Electrical Engineering, University of Management and Technology, Lahore, Pakistan.
J Neural Eng. 2018 Oct;15(5):056004. doi: 10.1088/1741-2552/aaceb1. Epub 2018 Jun 25.
The objective of the work described in this paper is the development of a computationally efficient methodology for patient-specific automatic seizure detection in long-term multi-channel EEG recordings.
A novel patient-specific seizure detection approach based on a signal-derived empirical mode decomposition (EMD)-based dictionary approach is proposed. For this purpose, we use an empirical framework for EMD-based dictionary creation and learning, inspired by traditional dictionary learning methods, in which the EMD-based dictionary is learned from the multi-channel EEG data being analyzed for automatic seizure detection. We present the algorithm for dictionary creation and learning, whose purpose is to learn dictionaries with a small number of atoms. Using training signals belonging to seizure and non-seizure classes, an initial dictionary, termed as the raw dictionary, is formed. The atoms of the raw dictionary are composed of intrinsic mode functions obtained after decomposition of the training signals using the empirical mode decomposition algorithm. The raw dictionary is then trained using a learning algorithm, resulting in a substantial decrease in the number of atoms in the trained dictionary. The trained dictionary is then used for automatic seizure detection, such that coefficients of orthogonal projections of test signals against the trained dictionary form the features used for classification of test signals into seizure and non-seizure classes. Thus no hand-engineered features have to be extracted from the data as in traditional seizure detection approaches.
The performance of the proposed approach is validated using the CHB-MIT benchmark database, and averaged accuracy, sensitivity and specificity values of 92.9%, 94.3% and 91.5%, respectively, are obtained using support vector machine classifier and five-fold cross-validation method. These results are compared with other approaches using the same database, and the suitability of the approach for seizure detection in long-term multi-channel EEG recordings is discussed.
The proposed approach describes a computationally efficient method for automatic seizure detection in long-term multi-channel EEG recordings. The method does not rely on hand-engineered features, as are required in traditional approaches. Furthermore, the approach is suitable for scenarios where the dictionary once formed and trained can be used for automatic seizure detection of newly recorded data, making the approach suitable for long-term multi-channel EEG recordings.
本文描述的工作的目的是开发一种计算效率高的方法，用于在长期多通道 EEG 记录中进行患者特异性自动癫痫发作检测。
提出了一种基于信号衍生经验模式分解（EMD）的字典方法的新型患者特异性癫痫发作检测方法。为此，我们使用了一种基于经验框架的 EMD 字典创建和学习方法，该方法受传统字典学习方法的启发，其中 EMD 字典是从正在分析的多通道 EEG 数据中学习的，用于自动癫痫发作检测。我们提出了字典创建和学习的算法，其目的是学习具有少量原子的字典。使用属于癫痫发作和非癫痫发作类别的训练信号，形成初始字典，称为原始字典。原始字典的原子由使用经验模式分解算法对训练信号进行分解后获得的固有模式函数组成。然后使用学习算法对原始字典进行训练，从而大大减少了训练字典中的原子数量。然后使用训练字典进行自动癫痫发作检测，使得测试信号与训练字典的正交投影系数形成用于将测试信号分类为癫痫发作和非癫痫发作类别的特征。因此，与传统的癫痫发作检测方法不同，无需从数据中提取手工设计的特征。
使用 CHB-MIT 基准数据库验证了所提出方法的性能，使用支持向量机分类器和五折交叉验证方法获得的平均准确率、灵敏度和特异性值分别为 92.9%、94.3%和 91.5%。这些结果与使用相同数据库的其他方法进行了比较，并讨论了该方法在长期多通道 EEG 记录中进行癫痫发作检测的适用性。
所提出的方法描述了一种计算效率高的方法，用于在长期多通道 EEG 记录中进行自动癫痫发作检测。该方法不依赖于传统方法所需的手工设计的特征。此外，该方法适用于一旦形成和训练字典即可用于新记录数据的自动癫痫发作检测的情况，因此适用于长期多通道 EEG 记录。
