深度学习框架用于使用皮电活动信号评估分类情绪状态
Praveen Kumar Govarthan1, Sriram Kumar P1, Nagarajan Ganapathy2
1School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
Studies in health technology and informatics
|June 30, 2023
概括
这项研究使用了皮电活动 (EDA) 信号和可配置卷积神经网络 (cCNN) 来分类情绪. 该方法达到80%以上的准确性,显示了在各种条件下分析情绪状态的潜力.
科学领域:
- 情感计算是一种情感计算.
- 物理计算的物理计算.
- 机器学习用于情绪识别和识别.
背景情况:
- 精确地分类情绪状态对于理解人类行为和心理健康至关重要.
- 电皮活动 (EDA) 信号为情绪检测提供了一个有希望的生理测量方法.
- 为实时情绪分类开发强大的机器学习模型仍然是一个挑战.
研究的目的:
- 用电皮活动 (EDA) 信号来分类类别的情绪状态 (有趣,无聊,放松,可怕).
- 开发和评估一种可配置的卷积神经网络 (cCNN) 模型,用于情绪歧视.
- 评估管道在正常和临床环境中分析情绪状态的有效性.
主要方法:
- 利用公开可用的持续注释情绪信号数据集.
- 经过处理的EDA信号:通过cvxEDA向下采样,分解成相位组件,以及用于谱图生成的短时间里叶变换.
- 采用可配置卷积神经网络 (cCNN) 嵌套k-Fold交叉验证用于模型培训和评估.
主要成果:
- 拟议的管道实现了高平均分类性能.
- 关键指标包括:准确率为80.20%,回忆率为60.41%,特异性为86.8%,精度为60.05%和F测量率为58.61%.
- 证明了模型的强度和区分不同情绪状态的能力.
结论:
- 开发的管道有效地使用EDA信号和cCNN对分类情绪状态进行分类.
- 这些发现表明,管道在检查各种情绪状态方面的潜在实用性.
- 这种方法对于典型和临床心理评估中的应用可能是有价值的.
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