使用概率二进制模式与心电图信号的自动焦虑检测
Mehmet Baygin1, Prabal Datta Barua2, Sengul Dogan3
1Department of Computer Engineering, Faculty of Engineering and Architecture, Erzurum Technical University, Erzurum, Turkey.
Computer methods and programs in biomedicine
|February 29, 2024
概括
使用心电图 (ECG) 信号的新模型准确地检测到超过98.5%的焦虑. 这种方法利用了一种新的概率二进制模式 (PBP) 功能工程方法,用于有效和可靠的焦虑症诊断.
科学领域:
- 生物医学工程 生物医学工程
- 计算神经科学是一种神经科学.
- 心脏病学 心脏病学
背景情况:
- 焦虑障碍很普遍,需要早期检测才能有效管理.
- 焦虑会影响生理系统,包括大脑和心脏.
- 电心电图 (ECG) 信号为心脏对焦虑的反应提供了一个窗口.
研究的目的:
- 开发一个高效和准确的手工制作的功能工程模型,用于自动化焦虑检测.
- 利用心电图信号来使用机器学习对焦虑程度进行分类.
主要方法:
- 利用了来自19名暴露在引起焦虑的视频中的受试者的开放式ECG数据.
- 采用了一种新的概率二进制模式 (PBP) 特性提取方法,与可调的q-因子波量变换相结合.
- 应用了缩小维度的技术 (邻近组件分析,Chi2) 和机器学习分类器 (k-NN,SVM) 用一个贪的算法进行最佳的模型选择.
主要成果:
- 在所有分析的心电图段长度 (4,5和6秒) 中,实现了超过98.5%的分类准确性.
- 废除研究证实了基于PBP的特征工程的优越性能,与局部二进制模式等传统方法相比.
结论:
- 开发的基于PBP的特征工程模型证明了使用心电图信号进行焦虑分类的高可行性和准确性.
- 这种自动化方法对焦虑症的客观和早期检测有希望.
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