用于癫痫发作预测的高效通道递归十字交叉注意网络

IF 1.7 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Lei Zhu , Wentao Wang , Aiai Huang , Nanjiao Ying , Ping Xu , Jianhai Zhang
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引用次数: 0

摘要

癫痫是一种由大脑神经元反复异常放电引起的慢性疾病。准确预测癫痫的发病可以有效改善患者的生活质量。虽然检测癫痫的方法有很多,但脑电图因其能提供丰富的大脑活动信息而被认为是目前最有效的分析工具之一。本研究的目的是从多通道癫痫脑电信号中探索潜在的时间频率和通道特征,并开发一个针对特定患者的癫痫发作预测网络。本文提出了一种癫痫脑电信号分类算法,称为通道递归交叉注意网络(CRCANet)。首先,将经过短时傅立叶变换处理的频谱图输入卷积神经网络(CNN)。然后,将上一步获得的频谱图特征图输入通道注意模块,以建立通道之间的相关性。随后,将包含通道关注特征的特征图输入递归十字关注模块,以增强每个像素的信息含量。最后,两个全连接层用于分类。我们在公开的 CHB-MIT 头皮脑电图数据集中对 13 名患者进行了验证,结果表明该方法的平均准确率为 93.8%,灵敏度为 94.3%,特异性为 93.5%。实验结果表明,CRCANet 能有效捕捉脑电信号的时频和信道特征,同时提高训练效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An efficient channel recurrent Criss-cross attention network for epileptic seizure prediction

Epilepsy is a chronic disease caused by repeated abnormal discharge of neurons in the brain. Accurately predicting the onset of epilepsy can effectively improve the quality of life for patients with the condition. While there are many methods for detecting epilepsy, EEG is currently considered one of the most effective analytical tools due to the abundant information it provides about brain activity. The aim of this study is to explore potential time-frequency and channel features from multi-channel epileptic EEG signals and to develop a patient-specific seizure prediction network. In this paper, an epilepsy EEG signal classification algorithm called Channel Recurrent Criss-cross Attention Network (CRCANet) is proposed. Firstly, the spectrograms processed by the short-time fourier transform is input into a Convolutional Neural Network (CNN). Then, the spectrogram feature map obtained in the previous step is input into the channel attention module to establish correlations between channels. Subsequently, the feature diagram containing channel attention characteristics is input into the recurrent criss-cross attention module to enhance the information content of each pixel. Finally, two fully connected layers are used for classification. We validated the method on 13 patients in the public CHB-MIT scalp EEG dataset, achieving an average accuracy of 93.8 %, sensitivity of 94.3 %, and specificity of 93.5 %. The experimental results indicate that CRCANet can effectively capture the time-frequency and channel characteristics of EEG signals while improving training efficiency.

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来源期刊
Medical Engineering & Physics
Medical Engineering & Physics 工程技术-工程:生物医学
CiteScore
4.30
自引率
4.50%
发文量
172
审稿时长
3.0 months
期刊介绍: Medical Engineering & Physics provides a forum for the publication of the latest developments in biomedical engineering, and reflects the essential multidisciplinary nature of the subject. The journal publishes in-depth critical reviews, scientific papers and technical notes. Our focus encompasses the application of the basic principles of physics and engineering to the development of medical devices and technology, with the ultimate aim of producing improvements in the quality of health care.Topics covered include biomechanics, biomaterials, mechanobiology, rehabilitation engineering, biomedical signal processing and medical device development. Medical Engineering & Physics aims to keep both engineers and clinicians abreast of the latest applications of technology to health care.
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