Weighted directed graph-based automatic seizure detection with effective brain connectivity for EEG signals

IF 2 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Signal Image and Video Processing Pub Date : 2023-10-19 DOI:10.1007/s11760-023-02816-4
Qi Sun, Yuanjian Liu, Shuangde Li
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Abstract

Abstract Epileptic seizure is one of the most common neurological disorders characterized by sudden abnormal discharge of neurons in the brain. Automated seizure detection using electroencephalograph (EEG) recordings would improve the quality of treatment and reduce medical overhead. The purpose of this paper is to design an automated seizure detection framework that can effectively identify seizure and non-seizure events by discovering connectivity between brain regions. In this work, a weighted directed graph-based method with effective brain connectivity (EBC) is proposed for seizure detection. The weighted directed graph is built by analyzing the correlation among the different regions of the brain. Then, graph theory-based measures are used to extract features for classification. Furthermore, we illustrate the ability of the proposed method to achieve seizure detection for the patient-specific model and the cross-patient model. The results show that the proposed method achieves accuracy values of 99.97% and 98.29% for the patient-specific model and the cross-patient model in the CHB-MIT dataset, respectively. These results demonstrate that the proposed method achieves an effective classification performance and can be used to provide assistance for automatic seizure detection and clinical diagnosis.
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基于有效脑连通性的加权有向图癫痫自动检测
癫痫发作是最常见的神经系统疾病之一,其特征是大脑神经元的突然异常放电。使用脑电图(EEG)记录自动检测癫痫发作将提高治疗质量并减少医疗开销。本文的目的是设计一个自动的癫痫检测框架,该框架可以通过发现大脑区域之间的连接来有效地识别癫痫和非癫痫事件。本文提出了一种基于有效脑连接(EBC)的加权有向图检测癫痫发作的方法。通过分析大脑不同区域之间的相关性,建立加权有向图。然后,利用基于图论的测度提取特征进行分类。此外,我们说明了所提出的方法能够实现针对特定患者模型和跨患者模型的癫痫检测。结果表明,该方法对CHB-MIT数据集中的患者特异性模型和跨患者模型的准确率分别达到99.97%和98.29%。结果表明,该方法具有良好的分类性能,可为癫痫发作的自动检测和临床诊断提供辅助。
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来源期刊
Signal Image and Video Processing
Signal Image and Video Processing ENGINEERING, ELECTRICAL & ELECTRONIC-IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
CiteScore
3.80
自引率
8.70%
发文量
328
审稿时长
6 months
期刊介绍: The journal is an interdisciplinary journal presenting the theory and practice of signal, image and video processing. It aims at: - Disseminating high level research results and engineering developments to all signal, image or video processing researchers and research groups. - Presenting practical solutions for the current signal, image and video processing problems in Engineering and Science. Subject areas covered by the journal include but are not limited to: Adaptive processing – biomedical signal processing – multimedia signal processing – communication signal processing – non-linear signal processing – array processing – statistics and statistical signal processing – modeling – filtering – data science – graph signal processing – multi-resolution signal analysis and wavelets – segmentation – coding – restoration – enhancement – storage and retrieval – colour and multi-spectral processing – scanning – displaying – printing – interpolation – image processing - video processing-motion detection and estimation – stereoscopic processing – image and video coding.
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