Efficient Seizure Detection by Complementary Integration of Convolutional Neural Network and Vision Transformer.

Jiaqi Wang, Haotian Li, Chuanyu Li, Weisen Lu, Haozhou Cui, Xiangwen Zhong, Shuhao Ren, Zhida Shang, Weidong Zhou
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Abstract

Epilepsy, as a prevalent neurological disorder, is characterized by its high incidence, sudden onset, and recurrent nature. The development of an accurate and real-time automatic seizure detection system is crucial for assisting clinicians in making precise diagnoses and providing timely treatment for epilepsy. However, conventional automatic seizure detection methods often face limitations in simultaneously capturing both local features and long-range correlations inherent in EEG signals, which constrains the accuracy of these existing detection systems. To address this challenge, we propose a novel end-to-end seizure detection framework, named CNN-ViT, which complementarily integrates a Convolutional Neural Network (CNN) for capturing local inductive bias of EEG and Vision Transformer (ViT) for further mining their long-range dependency. Initially, raw electroencephalogram (EEG) signals are filtered and segmented and then sent into the CNN-ViT model to learn their local and global feature representations and identify the seizure patterns. Meanwhile, we adopt a global max-pooling strategy to reduce the scale of the CNN-ViT model and make it focus on the most discriminative features. Given the occurrence of diverse artifacts in long-term EEG recordings, we further employ post-processing techniques to improve the seizure detection performance. The proposed CNN-ViT model, when evaluated using the publicly accessible CHB-MIT EEG dataset, reveals its outstanding performance with a sensitivity of 99.34% at a segment-based level and 99.70% at an event-based level. On the SH-SDU dataset we collected, our method yielded a segment-based sensitivity of 99.86%, specificity of 94.33%, and accuracy of 94.40%, along with an event-based sensitivity of 100%. The total processing time for 1[Formula: see text]h EEG data was only 3.07[Formula: see text]s. These exceptional results demonstrate the potential of our method as a reference for clinical real-time seizure detection applications.

通过卷积神经网络和视觉变换器的互补整合实现高效癫痫发作检测
癫痫是一种常见的神经系统疾病,其特点是发病率高、发作突然、反复发作。开发准确、实时的癫痫发作自动检测系统对于帮助临床医生准确诊断和及时治疗癫痫至关重要。然而,传统的癫痫自动检测方法往往面临着同时捕获EEG信号中固有的局部特征和远程相关性的局限性,这限制了现有检测系统的准确性。为了解决这一挑战,我们提出了一种新的端到端癫痫检测框架,名为CNN-ViT,它互补集成了卷积神经网络(CNN),用于捕获EEG和视觉变压器(ViT)的局部感应偏置,以进一步挖掘它们的远程依赖关系。首先,对原始脑电图(EEG)信号进行滤波和分割,然后送入CNN-ViT模型,学习其局部和全局特征表示,识别癫痫发作模式。同时,我们采用全局最大池化策略来减小CNN-ViT模型的规模,使其专注于最具判别性的特征。鉴于长期脑电图记录中出现各种伪影,我们进一步采用后处理技术来提高癫痫检测性能。当使用公开访问的CHB-MIT EEG数据集对所提出的CNN-ViT模型进行评估时,显示出其出色的性能,在基于片段的级别上灵敏度为99.34%,在基于事件的级别上灵敏度为99.70%。在我们收集的SH-SDU数据集上,我们的方法产生了基于片段的灵敏度为99.86%,特异性为94.33%,准确率为94.40%,以及基于事件的灵敏度为100%。1个[公式:见文]h个EEG数据的总处理时间仅为3.07[公式:见文]s。这些特殊的结果证明了我们的方法作为临床实时癫痫检测应用的参考的潜力。
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