EEG- symnet:基于多通道EEG信号的精神分裂症诊断,基于通道再校准和对称时空变压器网络

IF 5.4 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Naif Alsharabi , Rakesh Kumar Mahendran , Gharbi Alshammari
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引用次数: 0

摘要

精神分裂症是一种严重的精神障碍,其特征是思维、感知、情绪和行为扭曲,会严重损害个人日常生活的能力。早期和有效的诊断至关重要,因为它能够实现及时干预,从而改善结果并减轻患者和卫生保健系统的负担。现有的精神分裂症诊断研究经常面临与使用单通道频率脑电图信号相关的挑战,这可能会忽略多通道数据提供的关键信息,并限制模型准确捕捉大脑活动时空动态的能力。在本文中,我们提出了一种新的模型EEG- symnet:利用脑电信号诊断精神分裂症的时空通道再校准网络,旨在通过利用多通道频率脑电信号来提高精神分裂症的诊断。我们的方法从噪声校正和功率谱分析开始,提高脑电信号的质量,为后续的分析提供更可靠的基础。然后将预处理信号输入到我们的模型中,该模型由四个主要模块组成。首先,分层时空图神经网络(HSTG-Net)捕获跨通道的空间特征,从而全面理解脑电数据。接下来,多通道频率重新校准模块(CRFM)改进了模型解释复杂通道间关系的能力,增强了通道间通信。在此之后,对称转换器(symm - t)增强了学习到的特征中的时间关系,有效地捕获了与精神分裂症相关的时间依赖模式。最后,使用分类器层将信号准确分类为精神分裂症和非精神分裂症类别。STCR-Net模型的准确率为92.34%,灵敏度为91.78%,特异性为92.67%,F1-Score为0.92,AUC为0.95,显示出显著的诊断改善。我们提出的模型解决了现有的局限性,并为推进精神分裂症的诊断提供了一个强有力的框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EEG-SymNet: multi-channel EEG signal-based schizophrenia diagnosis using channel recalibration and symmetric spatial temporal transformer network
Schizophrenia is a severe mental disorder characterized by distorted thinking, perceptions, emotions, and behavior, which can significantly impair an individual’s ability to function in daily life. Early and effective diagnosis is crucial, as it enables timely intervention, which can improve outcomes and reduce the burden on patients and healthcare systems. Existing research in schizophrenia diagnosis often faces challenges related to the use of single-channel frequency EEG signals, which can overlook critical information provided by multi-channel data and limit the model’s ability to accurately capture the spatial and temporal dynamics of brain activity. In our proposed work, we introduce a novel model named EEG-SymNet: Spatial-Temporal Channel Recalibration Network for Schizophrenia Diagnosis using EEG signals, designed to enhance schizophrenia diagnosis through the utilization of multi-channel frequency EEG signals. Our approach begins with noise correction and power spectrum analysis to improve the quality of the EEG signals, ensuring a more reliable foundation for subsequent analysis. The pre-processed signals are then fed into our model, which consists of four main modules. First, Hierarchical Spatial Temporal Graph Neural Network (HSTG-Net) captures spatial features across channels, allowing for a comprehensive understanding of the EEG data. Next, the Multi-Channel Frequency Recalibration Module (CRFM) refines the model’s ability to interpret complex inter-channel relationships, enhancing inter-channel communication. Following this, Symmetric Transformer (Sym-T) enhances the learned temporal relations in the features, effectively capturing time-dependent patterns relevant to schizophrenia. Finally, a classifier layer is employed to provide accurate classification of the signals into schizophrenia and non-schizophrenia categories. The proposed STCR-Net model, which achieved an accuracy of 92.34 %, sensitivity of 91.78 %, specificity of 92.67 %, F1-Score of 0.92, and AUC of 0.95, demonstrating significant diagnostic improvements. Our proposed model addresses existing limitations and offers a robust framework for advancing the diagnosis of schizophrenia.
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来源期刊
Engineering Science and Technology-An International Journal-Jestech
Engineering Science and Technology-An International Journal-Jestech Materials Science-Electronic, Optical and Magnetic Materials
CiteScore
11.20
自引率
3.50%
发文量
153
审稿时长
22 days
期刊介绍: Engineering Science and Technology, an International Journal (JESTECH) (formerly Technology), a peer-reviewed quarterly engineering journal, publishes both theoretical and experimental high quality papers of permanent interest, not previously published in journals, in the field of engineering and applied science which aims to promote the theory and practice of technology and engineering. In addition to peer-reviewed original research papers, the Editorial Board welcomes original research reports, state-of-the-art reviews and communications in the broadly defined field of engineering science and technology. The scope of JESTECH includes a wide spectrum of subjects including: -Electrical/Electronics and Computer Engineering (Biomedical Engineering and Instrumentation; Coding, Cryptography, and Information Protection; Communications, Networks, Mobile Computing and Distributed Systems; Compilers and Operating Systems; Computer Architecture, Parallel Processing, and Dependability; Computer Vision and Robotics; Control Theory; Electromagnetic Waves, Microwave Techniques and Antennas; Embedded Systems; Integrated Circuits, VLSI Design, Testing, and CAD; Microelectromechanical Systems; Microelectronics, and Electronic Devices and Circuits; Power, Energy and Energy Conversion Systems; Signal, Image, and Speech Processing) -Mechanical and Civil Engineering (Automotive Technologies; Biomechanics; Construction Materials; Design and Manufacturing; Dynamics and Control; Energy Generation, Utilization, Conversion, and Storage; Fluid Mechanics and Hydraulics; Heat and Mass Transfer; Micro-Nano Sciences; Renewable and Sustainable Energy Technologies; Robotics and Mechatronics; Solid Mechanics and Structure; Thermal Sciences) -Metallurgical and Materials Engineering (Advanced Materials Science; Biomaterials; Ceramic and Inorgnanic Materials; Electronic-Magnetic Materials; Energy and Environment; Materials Characterizastion; Metallurgy; Polymers and Nanocomposites)
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