Kaleem Muhammad, Guergachi Aziz, Krishnan Sridhar
Department of Electrical Engineering, University of Management and Technology, Lahore, Pakistan.
Department of Information Technology Management, Ted Rogers School of Management, Ryerson University, Toronto, ON, Canada.
Front Digit Health. 2021 Dec 13;3:738996. doi: 10.3389/fdgth.2021.738996. eCollection 2021.
Analysis of long-term multichannel EEG signals for automatic seizure detection is an active area of research that has seen application of methods from different domains of signal processing and machine learning. The majority of approaches developed in this context consist of extraction of hand-crafted features that are used to train a classifier for eventual seizure detection. Approaches that are data-driven, do not use hand-crafted features, and use small amounts of patients' historical EEG data for classifier training are few in number. The approach presented in this paper falls in the latter category, and is based on a signal-derived empirical dictionary approach, which utilizes empirical mode decomposition (EMD) and discrete wavelet transform (DWT) based dictionaries learned using a framework inspired by traditional methods of dictionary learning. Three features associated with traditional dictionary learning approaches, namely projection coefficients, coefficient vector and reconstruction error, are extracted from both EMD and DWT based dictionaries for automated seizure detection. This is the first time these features have been applied for automatic seizure detection using an empirical dictionary approach. Small amounts of patients' historical multi-channel EEG data are used for classifier training, and multiple classifiers are used for seizure detection using newer data. In addition, the seizure detection results are validated using 5-fold cross-validation to rule out any bias in the results. The CHB-MIT benchmark database containing long-term EEG recordings of pediatric patients is used for validation of the approach, and seizure detection performance comparable to the state-of-the-art is obtained. Seizure detection is performed using five classifiers, thereby allowing a comparison of the dictionary approaches, features extracted, and classifiers used. The best seizure detection performance is obtained using EMD based dictionary and reconstruction error feature and support vector machine classifier, with accuracy, sensitivity and specificity values of 88.2, 90.3, and 88.1%, respectively. Comparison is also made with other recent studies using the same database. The methodology presented in this paper is shown to be computationally efficient and robust for patient-specific automatic seizure detection. A data-driven methodology utilizing a small amount of patients' historical data is hence demonstrated as a practical solution for automatic seizure detection.
分析长期多通道脑电图信号以进行自动癫痫发作检测是一个活跃的研究领域，该领域已经应用了来自信号处理和机器学习不同领域的方法。在这种情况下开发的大多数方法包括提取手工制作的特征，这些特征用于训练分类器以最终进行癫痫发作检测。数据驱动、不使用手工制作的特征且使用少量患者历史脑电图数据进行分类器训练的方法数量很少。本文提出的方法属于后一类，它基于一种信号衍生的经验字典方法，该方法利用经验模态分解（EMD）和基于离散小波变换（DWT）的字典，这些字典是使用受传统字典学习方法启发的框架学习得到的。从基于EMD和DWT的字典中提取与传统字典学习方法相关的三个特征，即投影系数、系数向量和重构误差，用于自动癫痫发作检测。这是首次将这些特征应用于基于经验字典方法的自动癫痫发作检测。使用少量患者的历史多通道脑电图数据进行分类器训练，并使用多个分类器对新数据进行癫痫发作检测。此外，使用五折交叉验证对癫痫发作检测结果进行验证，以排除结果中的任何偏差。使用包含儿科患者长期脑电图记录的CHB-MIT基准数据库对该方法进行验证，并获得了与当前最先进技术相当的癫痫发作检测性能。使用五个分类器进行癫痫发作检测，从而可以比较字典方法、提取的特征和使用的分类器。使用基于EMD的字典和重构误差特征以及支持向量机分类器获得了最佳癫痫发作检测性能，准确率、灵敏度和特异性值分别为88.2%、90.3%和88.1%。还与使用同一数据库的其他近期研究进行了比较。本文提出的方法被证明对于特定患者的自动癫痫发作检测在计算上是高效且稳健的。因此，一种利用少量患者历史数据的数据驱动方法被证明是自动癫痫发作检测的一种实用解决方案。
