LCADNet:一种基于EEG的轻型CNN架构,用于基于EEG的阿尔茨海默病检测
Pramod Kachare1, Digambar Puri1, Sandeep B Sangle1
1Department of Electronics and Telecommunication, Ramrao Adik Institute of Technology, D. Y. Patil Campus, Navi-Mumbai, Maharashtra, 400706, India.
Physical and engineering sciences in medicine
|June 11, 2024
概括
一个新的轻量级卷积神经网络,LCADNet,通过电脑电图 (EEG) 信号提供准确和快速的阿尔茨海默病检测. 这种人工智能模型显著改进了现有的早期阿尔茨海默病诊断方法.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 阿尔茨海默病 (AD) 诊断是具有挑战性的,因为它的渐进性和当前方法的局限性.
- 使用脑电图 (EEG) 信号的现有自动化方法往往缺乏准确性和可靠性.
- 需要有效和精确的自动化AD检测技术.
研究的目的:
- 开发和评估一个轻量级卷积神经网络 (LCADNet),用于准确和快速的阿尔茨海默病检测.
- 将LCADNet的性能与使用转移学习用于AD检测的预训练模型进行比较.
- 评估LCADNet在多个数据集中的通用化能力.
主要方法:
- 设计了一个新的轻量级卷积神经网络 (LCADNet) 架构,结合了卷积,完全连接和最大聚合层.
- 来自公开可用的数据集的EEG数据被用于训练和测试LCADNet模型.
- 将LCADNet的效率和分类性能与使用转移学习的四个预训练模型进行了比较.
- 通过对两个额外的AD检测数据集进行模型交叉测试来评估概括性.
主要成果:
- LCADNet展示了最低的计算复杂性 (浮点运算和推理时间).
- 该模型在六个标准指标中实现了最高的分类性能.
- 交叉测试证实了LCADNet在未见的数据集上强大的概括能力.
- 在基于EEG的AD检测中,LCADNet取得了98.50%的卓越准确性.
结论:
- 通过使用EEG信号,LCADNet提供了一种高度准确和计算高效的阿尔茨海默病检测方法.
- 开发的模型优于现有的基于EEG的AD检测方法.
- 在临床诊断中,LCADNet显示出作为一个有价值的工具来协助神经病学家的潜力.
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