Department of Neurosurgery, School for Mental Health and Neuroscience of the Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; Center for Medical Electronics and Computing, MS Ramaiah Institute of Technology, Bengaluru, India.
Center for Medical Electronics and Computing, MS Ramaiah Institute of Technology, Bengaluru, India.
Clin Neurophysiol. 2020 Jul;131(7):1567-1578. doi: 10.1016/j.clinph.2020.03.033. Epub 2020 Apr 23.
In long-term electroencephalogram (EEG) signals, automated classification of epileptic seizures is desirable in diagnosing epilepsy patients, as it otherwise depends on visual inspection. To the best of the author's knowledge, existing studies have validated their algorithms using cross-validation on the same database and less number of attempts have been made to extend their work on other databases to test the generalization capability of the developed algorithms. In this study, we present the algorithm for cross-database evaluation for classification of epileptic seizures using five EEG databases collected from different centers. The cross-database framework helps when sufficient epileptic seizures EEG data are not available to build automated seizure detection model.
Two features, namely successive decomposition index and matrix determinant were extracted at a segmentation length of 4 s (50% overlap). Then, adaptive median feature baseline correction (AM-FBC) was applied to overcome the inter-patient and inter-database variation in the feature distribution. The classification was performed using a support vector machine classifier with leave-one-database-out cross-validation. Different classification scenarios were considered using AM-FBC, smoothing of the train and test data, and post-processing of the classifier output.
Simulation results revealed the highest area under the curve-sensitivity-specificity-false detections (per hour) of 1-1-1-0.15, 0.89-0.99-0.82-2.5, 0.99-0.73-1-1, 0.95-0.97-0.85-1.7, 0.99-0.99-0.92-1.1 using the Ramaiah Medical College and Hospitals, Children's Hospital Boston-Massachusetts Institute of Technology, Temple University Hospital, Maastricht University Medical Centre, and University of Bonn databases respectively.
We observe that the AM-FBC plays a significant role in improving seizure detection results by overcoming inter-database variation of feature distribution.
To the best of the author's knowledge, this is the first study reporting on the cross-database evaluation of classification of epileptic seizures and proven to be better generalization capability when evaluated using five databases and can contribute to accurate and robust detection of epileptic seizures in real-time.
在长时间的脑电图（EEG）信号中，对癫痫发作进行自动分类在诊断癫痫患者方面是理想的，因为否则需要进行视觉检查。据作者所知，现有研究已经在同一数据库上使用交叉验证验证了他们的算法，并且尝试将其工作扩展到其他数据库以测试开发算法的泛化能力的次数较少。在这项研究中，我们提出了一种使用来自不同中心的五个 EEG 数据库进行癫痫发作分类的跨数据库评估算法。当没有足够的癫痫发作 EEG 数据来构建自动检测模型时，跨数据库框架很有帮助。
在 4 秒（50%重叠）的分段长度下提取两个特征，即连续分解指数和矩阵行列式。然后，应用自适应中值特征基线校正（AM-FBC）来克服特征分布中的个体间和个体间差异。使用支持向量机分类器进行分类，并采用数据库外留一交叉验证。使用 AM-FBC、训练数据和测试数据的平滑以及分类器输出的后处理考虑了不同的分类情况。
模拟结果显示，Ramaiah 医疗中心和医院、波士顿麻省理工学院儿童医院、坦普尔大学医院、马斯特里赫特大学医学中心和波恩大学数据库的曲线下面积-灵敏度-特异性-假阳性率（每小时）最高值分别为 1-1-1-0.15、0.89-0.99-0.82-2.5、0.99-0.73-1-1、0.95-0.97-0.85-1.7 和 0.99-0.99-0.92-1.1。
我们观察到 AM-FBC 通过克服特征分布的数据库间差异，在提高癫痫发作检测结果方面发挥了重要作用。
据作者所知，这是第一项报告关于癫痫发作分类的跨数据库评估的研究，并已证明在使用五个数据库进行评估时具有更好的泛化能力，有助于实时准确和稳健地检测癫痫发作。
