基于心率变量的心电图多情感识别信号特征采矿采矿
Ling Wang1, Jiayu Hao1, Tie Hua Zhou1
1Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin 132013, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究引入了一种使用心率变化 (HRV) 来识别情绪的新方法. 通过分析本地HRV信号特征,HRV情感识别 (HER) 方法实现了84.3%的准确性.
科学领域:
- 生理学 生理学 生理学
- 心理学 心理学 心理学
- 计算机科学 计算机科学
背景情况:
- 心率变化 (HRV) 反映了自主神经系统的功能及其与情绪的联系.
- 当前的HRV情绪识别方法往往忽略了局部信号特征,限制了信息利用.
研究的目的:
- 通过增强特征提取,提出一种使用HRV进行情感识别的有效方法.
- 为了提高从生理信号识别情绪的准确性和效率.
主要方法:
- 开发了HRV情感识别 (HER) 方法,将广度级量化 (ALQ) 纳入特征提取.
- 使用情绪量化分析 (EQA) 来评估情绪兴奋.
- 使用后勤回归 (LR) 来对提取的HRV特征进行分类.
主要成果:
- 提出的 HER 方法在情绪识别方面实现了 84.3% 的平均准确率.
- HER方法有效地提取和利用不同频率范围内的HRV信号中的局部特征.
- 与现有的基于HRV的情绪识别技术相比,表现出优异的性能.
结论:
- HER方法提供了一种高效和准确的方法,用于使用HRV识别情绪.
- 这种技术增强了本地HRV信号信息的利用,以更好地检测情绪.
- 为心理学,医学和情感计算领域的研究提供了宝贵的支持.
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