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从使用连续波形变换基于深度卷积神经网络模型的EEG信号来诊断

Fırat Dişli¹, Mehmet Gedikpınar¹, Hüseyin Fırat²

¹Department of Electrical and Electronic Engineering, Faculty of Technology, Firat University, 23000 Elazig, Turkey.

Diagnostics (Basel, Switzerland)

|January 11, 2025

概括

这项研究引入了一种新的深度学习模型,用于使用电脑电图 (EEG) 图像自动诊断. 开发的系统实现了高精度,为神经病学家提供了一个有前途的工具.

科学领域:

神经学神经学
人工智能的人工智能
信号处理信号处理

背景情况:

是一种常见的神经系统疾病,导致发作,需要可靠的诊断工具.
由于发作的不可预测性质,自动诊断系统至关重要.
脑电图 (EEG) 信号分析是关键,深度学习提供了先进的功能.

研究的目的:

开发一种使用新型深度学习方法的自动诊断系统.
为了利用连续波波变换和深度卷积神经网络 (DCNNs) 进行EEG分析.
为了提高诊断准确性和发病检测的效率.

主要方法:

脑电图信号被用连续波形变换转换成图像.
图像被连接为一个DCNN模型的单个输入.
一个DCNN接受了诊断的培训和评估.

主要成果:

该DCNN模型实现了高性能指标:95.99%的准确性,94.27%的灵敏性,97.29%的特异性和96.34%的精度.
对比分析显示,相对于现有方法,性能优于现有方法.
图像连接技术在DCNN输入中被证明是有效的.

结论:

拟议的DCNN模型与图像连接提供了一种新且有效的诊断方法.
这种方法消除了对额外分类器或特征选择的需求.
该系统为支持神经病学家在诊断中提供了一个有价值的工具,并且可以适应其他数据集.

关键词:

连续波形变换连续波形变换.深度向上的卷积.是一种.图像连接连接连接连接

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