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Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN

机译：基于改进的1-D CNN和Indrnn的剩余剩余网络识别研究

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Epilepsy is one of the diseases of the nervous system, which has a large population in the world. Traditional diagnosis methods mostly depended on the professional neurologists’ reading of the electroencephalogram (EEG), which was time-consuming, inefficient, and subjective. In recent years, automatic epilepsy diagnosis of EEG by deep learning had attracted more and more attention. But the potential of deep neural networks in seizure detection had not been fully developed. In this article, we used a one-dimensional convolutional neural network (1-D CNN) to replace the residual network architecture’s traditional convolutional neural network (CNN). Moreover, we combined the Independent recurrent neural network (indRNN) and CNN to form a new residual network architecture-independent convolutional recurrent neural network (RCNN). Our model can achieve an automatic diagnosis of epilepsy EEG. Firstly, the important features of EEG were learned by using the residual network architecture of 1-D CNN. Then the relationship between the sequences were learned by using the recurrent neural network. Finally, the model outputted the classification results. On the small sample data sets of Bonn University, our method was superior to the baseline methods and achieved 100% classification accuracy, 100% classification specificity. For the noisy real-world data, our method also exhibited powerful performance. The model we proposed can quickly and accurately identify the different periods of EEG in an ideal condition and the real-world condition. The model can provide automatic detection capabilities for clinical epilepsy EEG detection. We hoped to provide a positive significance for the prediction of epileptic seizures EEG.
机译：癫痫是神经系统的疾病之一，世界上具有大量的人口。传统的诊断方法主要取决于专业神经科医生的脑电图（EEG）的读数，这是耗时，低效和主观的。近年来，深入学习的脑电图诊断越来越受到关注。但是癫痫发作检测中深度神经网络的潜力尚未完全开发。在本文中，我们使用一维卷积神经网络（1-D CNN）来取代残余网络架构的传统卷积神经网络（CNN）。此外，我们将独立的经常性神经网络（INDRNN）和CNN组合形成一个新的残余网络架构无关的卷积复发性神经网络（RCNN）。我们的模型可以实现癫痫脑电图的自动诊断。首先，通过使用1-D CNN的残余网络架构来学习EEG的重要特征。然后通过使用经常性神经网络学习序列之间的关系。最后，该模型输出了分类结果。在Bonn大学的小样本数据集上，我们的方法优于基线方法，实现了100％的分类精度，100％的分类特异性。对于嘈杂的现实世界数据，我们的方法也表现出强大的表现。我们提出的模型可以快速准确地识别理想状态和真实情况的脑梗死的不同时期。该模型可以为临床癫痫EEG检测提供自动检测能力。我们希望为癫痫发作脑卒中的预测提供积极意义。

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