TQCPat:基于树量子电路模式的特征工程模型,用于使用PPG信号自动检测心律失常.
Mehmet Ali Gelen1, Turker Tuncer2, Mehmet Baygin3
1Department of Cardiology, Elazig Fethi Sekin City Hospital, Elazig, Turkey.
Journal of medical systems
|March 24, 2025
概括
这项研究引入了一种新的树量子电路模式 (TQCPat) 模型,用于使用光聚光学 (PPG) 信号准确检测心律失常. 该TQCPat模型在分类六种心律失常类型时实现了91.30%的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 量子计算应用 量子计算应用
背景情况:
- 心律失常带来了显著的发病率和死亡率风险.
- 摄影脉冲图 (PPG) 信号为评估血液流动和检测心脏不规则提供了一种非侵入性方法.
- 开发准确且具有成本效益的心律失常检测方法至关重要.
研究的目的:
- 提出一种新的,自我组织的功能工程模型来检测心律失常.
- 为了提高诊断能力,利用简单,具有成本效益的光聚体显微镜 (PPG) 信号.
- 开发一个准确的系统来分类不同类型的心律失常.
主要方法:
- 使用离散波量变换 (MDWT) 和量子启发的树量子电路模式 (TQCPat) 的特征提取.
- 采用千平方 (Chi2) 和邻近组件分析 (NCA) 的特征选择.
- 使用k-近邻 (kNN) 和支持向量机 (SVM) 进行信息融合的分类.
主要成果:
- 基于TQCPat的特征工程模型实现了91.30%的分类准确度.
- 该模型在46,827个PPG信号的大数据集上得到了验证.
- 六个不同的类别的心律失常被分类为十倍的交叉验证.
结论:
- 拟议的TQCPat模型证明了使用PPG信号进行心律失常分类的高准确性.
- 该模型的有效性表明,该模型可能具有更广泛的临床应用.
- 建议使用更大的数据库和额外的心律失常类别进行进一步验证.
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