在中应用的深度学习:图书识别和视觉分析
Digital health
|September 15, 2025
概括
通过分析电脑电图 (EEG) 数据,深度学习 (DL) 显示出对管理的希望. 这份图书统计分析揭示了症DL的关键研究趋势和热点,指导了发作检测和预测的未来进展.
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:
- 是一种常见的神经系统疾病,对患者有重大影响.
- 传统的诊断和机器学习方法面临着复杂的脑电图 (EEG) 数据的挑战.
- 深度学习 (DL) 通过EEG分析和脑成像提供了通过EEG分析和脑成像改善管理的潜力.
研究的目的:
- 对研究中的深度学习应用进行视觉图库分析.
- 确定该领域的研究趋势,热点和新兴发展.
- 为了指导未来的研究方向在DL管理.
主要方法:
- 在科学网络核心集合 (2006-2025) 上进行了全面的文献搜索.
- 使用CiteSpace,VOSviewer和Bibliometrix进行了圣经测量分析和可视化.
- 包含了原始的英语研究文章和评论.
主要成果:
- 总共有1266篇关于症中DL的论文被确定,显示出一致的上升研究趋势.
- 研究集中在中国和美国",生物医学信号处理和控制"作为主要期刊.
- 关键的研究热点包括用于发作检测,预测和管理的DL,对多式联络数据集成的兴趣日益增加.
结论:
- 图书统计分析强调了DL在研究中的日益重要.
- DL应用程序显示了增强发作检测和预测准确性的潜力,改善了患者的生活质量.
- 未来的研究应该专注于改善预测,整合多式联络数据,并开发可解释的DL模型.
关键词:
深度学习是一种深度学习.这是一个EEGEEGEEGEEGEEGEEGEEG.圣经计量资料 (Bibliometric bibliometric bibliometric) 是指参考书目中的资料.卷积神经网络是一种卷积神经网络.是一种.发作检测检测 发作检测发作预测预测预测更多相关视频
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