通过将多模式特征与双输入深度学习方法集成来识别药物使用程度
Yuxing Zhou1, Xuelin Gu1, Zhen Wang1
1College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China.
Computer methods in biomechanics and biomedical engineering
|October 29, 2024
概括
这项研究引入了一种新的双输入双模融合算法,使用电脑图 (EEG) 和近红外光谱 (NIRS) 进行客观的药物使用程度评估. 拟议的方法实现了高分类准确性,优于单模式方法.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 目前的药物使用程度评估严重依赖于主观判断.
- 缺乏评估药物使用程度的客观,定量方法.
- 整合神经成像和生理信号为客观评估提供了一个有希望的途径.
研究的目的:
- 提出和评估双输入双模融合算法,用于对药物使用程度的客观定量评估.
- 利用电脑电图 (EEG) 和近红外光谱 (NIRS) 信号来加强药物使用程度的分类.
- 为了比较双模融合与单模融合方法的性能.
主要方法:
- 使用优化的双输入多模式TiCBnet从EEG和NIRS信号中进行深度特征提取.
- 采用各种特征融合和选技术来结合双模数据.
- 分类了融合的深度编码特征,以确定药物使用程度.
主要成果:
- 与单模方法相比,双模融合方法显示出更高的分类准确性.
- 拟议的算法实现了高达89.9%的分类准确性.
- 功能融合和选对于优化双模系统性能至关重要.
结论:
- EEG和NIRS信号的双输入双模融合提供了一个客观而准确的方法来评估药物使用程度.
- TiCBnet架构有效地从多式联网数据中提取和融合深层特征.
- 这种方法比主观和单模评估方法有了显著的进步.
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