癫痫的及时诊断依赖于自动脑电图(EEG)信号的准确分类。已经开发了几种方法来表征癫痫EEG数据;然而,他们都没有利用时频数据来评估在预训练的EEG数据分类框架中调整参数的效果。这项研究比较了几种预训练的卷积神经网络(CNN)的性能,即AlexNet,GoogLeNet,MobileNetV2,ResNet-18和SqueezeNet使用各种时频数据表示算法对癫痫脑电图数据进行定位。连续小波变换(CWT),经验傅里叶分解(EFD),经验模态分解(EMD),经验小波变换(EWT),和变分模式分解(VMD)被用于从一维数据中获取二维谱图。该研究评估了多个因素的影响,包括嘈杂和去噪的图像,不同的优化器,学习率,单通道与双通道,型号尺寸,和计算时间消耗。基准伯尔尼巴塞罗那脑电图数据集用于测试目的。获得的结果表明,MobileNetV2,连续小波变换(CWT)和Adam优化器的组合在10^-4的学习率下,加上双数据通道,提供最佳性能指标。具体来说,这些参数导致最佳灵敏度,特异性,f1-score,和分类精度,各自的值为96.06%,96.15%,96.08%,和96.10%。为了进一步证实选择的预训练模型对利用的信号分解(SD)算法的有效性,分类器也在TempleUniversity数据库的pinnacle建模组成中进行了模拟。结果中的类似模式很容易验证我们的研究结果和深度学习模型对癫痫脑电图的鲁棒性。得出的结论强调了预训练的基于CNN的模型的潜力,诊断癫痫样的自动化系统。此外,该研究提供了不同时频技术和分类器参数对癫痫脑电图数据分类的有效性的见解。
Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed to characterize epileptic EEG data; however, none of them have exploited time-frequency data to evaluate the effect of tweaking parameters in pretrained frameworks for EEG data classification. This
study compares the performance of several pretrained convolutional neural networks (CNNs) namely, AlexNet, GoogLeNet, MobileNetV2, ResNet-18 and SqueezeNet for the localization of epilepsy EEG data using various time-frequency data representation algorithms. Continuous wavelet transform (CWT), empirical Fourier decomposition (EFD), empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) were exploited for the acquisition of 2D scalograms from 1D data. The research evaluates the effect of multiple factors, including noisy versus denoised scalograms, different optimizers, learning rates, single versus dual channels, model size, and computational time consumption. The benchmark Bern-Barcelona EEG dataset is used for testing purpose. Results obtained show that the combination of MobileNetV2, Continuous Wavelet Transform (CWT) and Adam optimizer at a learning rate of 10-4, coupled with dual-data channels, provides the best performance metrics. Specifically, these parameters result in optimal sensitivity, specificity, f1-score, and classification accuracy, with respective values of 96.06%, 96.15%, 96.08%, and 96.10%. To further corroborate the efficacy of opted pretrained models on exploited Signal Decomposition (SD) algorithms, the classifiers are also being simulated on Temple University database at pinnacle modeling composition. A similar pattern in the outcome readily validate the findings of our
study and robustness of deep learning models on epilepsy EEG scalograms.The conclusions drawn emphasize the potential of pretrained CNN-based models to create a robust, automated system for diagnosing epileptiform. Furthermore, the
study offers insights into the effectiveness of varying time-frequency techniques and classifier parameters for classifying epileptic EEG data.