一种基于机器学习的精细SMOTE-ENN优化方法,用于心率变化数据分类
Biao Zhang1,2, Muzi Liang1, Yuanlun Zhou1
1School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China.
Frontiers in digital health
|March 2, 2026
概括
一种新的机器学习方法提炼了不平衡的心率变化 (HRV) 数据以检测抑郁症. 这种方法改善了自主神经系统 (ANS) 状态的分类,帮助早期诊断.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 计算神经科学是一种神经科学.
背景情况:
- 不平衡的心率变化 (HRV) 数据对机器学习模型在抑郁症检测方面提出了挑战.
- 早期识别抑郁症至关重要,可以通过分析自主神经系统 (ANS) 状态来支持.
研究的目的:
- 提出一种精细的SMOTE-ENN混合优化方法,用于精确分类不平衡的HRV数据.
- 通过HRV分析提高机器学习算法性能,用于早期抑郁症检测.
主要方法:
- 开发了一种精细的合成少数群体过量采样技术 (SMOTE) 和编辑近邻 (ENN) 过少采样算法.
- 四个机器学习算法 (SVM,随机森林,神经网络,KNN) 应用于来自321名参与者的优化HRV数据.
主要成果:
- 所有四个机器学习算法都实现了超过91%的分类准确度,在精细的SMOTE-ENN优化后,AUC值超过0.92.
- 与经典的SMOTE相比,这种精细的方法在准确性,精度,回忆力和F1分数方面显著改善.
- 在HRV分类中,NN间隔的标准偏差 (SDNN) 被确定为最有影响力的特征.
结论:
- 精细的SMOTE-ENN方法有效地提高了不平衡HRV数据分类的机器学习性能.
- 这种方法通过改进的ANS状态分析,为早期发现抑郁症提供了有价值的技术支持.
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