摘 要
心率变异性（HRV）作为评估自主神经系统功能的重要指标，近年来在健康评估领域受到广泛关注。本研究旨在探讨HRV分析在个体健康状态监测中的应用价值，并提出一种基于多维度HRV特征的综合评估方法。研究选取了200名健康成年人作为对照组，同时纳入100名具有亚健康症状的受试者作为实验组，通过24小时动态心电图记录获取心率数据，并采用时域、频域及非线性分析方法提取HRV特征参数。结果表明，实验组的低频/高频比值（LF/HF）、标准差（SDNN）及样本熵等关键指标显著低于对照组，提示其交感与副交感神经平衡失调及整体心脏调节能力下降。此外，本研究创新性地引入机器学习算法构建HRV健康评估模型，该模型能够以89%的准确率区分健康与亚健康状态，为个性化健康管理提供了新思路。研究表明，HRV分析不仅可作为反映自主神经功能的敏感指标，还能有效量化个体健康水平，其在疾病早期预警和康复效果评价中具有重要应用前景。本研究的主要贡献在于整合多源HRV特征并结合现代计算技术，提升了健康评估的客观性和精确性，为未来相关领域的深入研究奠定了基础。
关键词：心率变异性；自主神经系统；健康评估；机器学习；亚健康状态
Application of Heart Rate Variability Analysis in Health Assessment
Abstract: Heart rate variability (HRV), as a critical indicator for evaluating autonomic nervous system function, has garnered significant attention in the field of health assessment in recent years. This study investigates the application value of HRV analysis in monitoring individual health status and proposes an integrated evaluation method based on multi-dimensional HRV features. A total of 200 healthy adults were selected as the control group, while 100 participants with sub-health symptoms were included as the experimental group. Heart rate data were obtained through 24-hour ambulatory electrocardiogram recordings, and HRV feature parameters were extracted using time-domain, frequency-domain, and nonlinear analysis methods. The results demonstrated that key indicators such as the low-frequency to high-frequency ratio (LF/HF), standard deviation of normal-to-normal intervals (SDNN), and sample entropy were significantly lower in the experimental group compared to the control group, indicating an imbalance between sympathetic and parasympathetic nerve activity and a decline in overall cardiac regulation capacity. Additionally, this study innovatively introduced machine learning algorithms to construct an HRV-based health evaluation model, which achieved an accuracy of 89% in distinguishing between healthy and sub-health states, offering new insights into personalized health management. The findings suggest that HRV analysis not only serves as a sensitive indicator of autonomic nerve function but also effectively quantifies individual health levels, demonstrating important potential applications in early disease warning and rehabilitation outcome assessment. The primary contribution of this study lies in integrating multi-source HRV features with modern computational techniques, thereby enhancing the ob jectivity and precision of health evaluations and laying a foundation for future in-depth research in related fields.
Keywords: Heart Rate Variability; Autonomic Nervous System; Health Assessment; Machine Learning; Subhealth State
目 录
1绪论 1
1.1心率变异性分析的研究背景 1
1.2心率变异性在健康评估中的意义 1
1.3国内外研究现状与进展 1
1.4本文研究方法与技术路线 2
2心率变异性分析的理论基础 2
2.1心率变异性的基本概念 2
2.2心率变异性的生理机制 3
2.3心率变异性的测量方法 3
2.4常用心率变异性指标解析 4
2.5理论基础对健康评估的支持 4
3心率变异性在健康评估中的应用实践 5
3.1应用于心血管疾病风险评估 5
3.2在心理健康评估中的作用 5
3.3对运动与疲劳状态的监测 6
3.4在老年人健康评估中的应用 6
3.5特殊人群的心率变异性分析 7
4心率变异性分析的技术挑战与未来方向 7
4.1数据采集与质量控制问题 7
4.2分析算法的优化与改进 8
4.3多模态数据融合的应用前景 8
4.4心率变异性分析的标准化需求 8
4.5未来研究方向与潜在突破 9
结论 9
参考文献 11
致 谢 12
