A convolutional neural network based framework for classification of seizure types
A convolutional neural network based framework for classification of seizure types
复制标题
基于卷积神经网络的癫痫类型分类框架
DOI：
10.1109/embc.2019.8857359
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
P. Kubben
中科院分区：
文献类型：
--
作者：
S. Raghu;N. Sriraam;Y. Temel;S. V. Rao;P. Kubben
癫痫发作是由大脑电活动的干扰引起的，并根据EEG和其他参数的特征被分类为许多不同类型的癫痫发作。迄今为止，已有研究将脑电图分为发作性和非发作性两类，但对发作类型的分类尚未进行探讨。因此，在本文中，我们提出了8类分类问题，以便使用卷积神经网络（CNN）对不同的癫痫发作类型进行分类。这项研究提出了一个基于CNN的癫痫发作类型分类框架，包括简单部分性、复杂部分性、局灶性非特异性、全身性非特异性、失神、强直性、强直阵挛性和非癫痫发作。将EEG时间序列转换为频谱图堆栈并用作CNN的输入。据作者所知，我们是第一个使用计算算法对癫痫发作类型进行分类的研究。采用AlexNet、VGG16、VGG19和基本CNN模型研究了8类分类问题的性能。该研究表明，使用AlexNet，VGG16，VGG19和基本CNN模型的分类准确率分别为84.06%，79.71%，76.81%和82.14%。实验结果表明，所提出的框架可能有助于神经学社区识别癫痫发作类型。
Epileptic seizures are caused by a disturbance in the electrical activity of the brain and classified as many different types of epileptic seizures based on the characteristics of EEG and other parameters. Till now research has been conducted to classify EEG as seizure and non-seizures, but the classification of seizure types has not been explored. Thus, in this paper, we have proposed the 8-class classification problem in order to classify different seizure types using convolutional neural networks (CNN). This research study suggests a CNN based framework for classification of epileptic seizure types that include simple partial, complex partial, focal non-specific, generalized non-specific, absence, tonic, and tonic-clonic, and non-seizures. EEG time series was converted into spectrogram stacks and used as input for CNN. To the best of authors knowledge, ours is the very first study that classified the seizures types using the computational algorithm. The four CNN models, namely AlexNet, VGG16, VGG19, and basic CNN model was applied to study the performance of 8-class classification problem. The proposed study showed a classification accuracy of 84.06%, 79.71%, 76.81%, and 82.14% using AlexNet, VGG16, VGG19 and basic CNN models respectively. The experimental results suggest that the proposed framework could be helpful to the neurology community for recognition of seizures types.
DOI：
10.1080/21681163.2016.1141062
发表时间：
2018-01-01
影响因子：
1.6
作者：
Achilles, Felix;Tombari, Federico;Navab, Nassir
通讯作者：
Navab, Nassir