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心率变化的循环节律调节作为主要抑郁症潜在生物标志物:一种机器学习方法

Ye Xia¹, Han Zhang¹, Ziwei Wang¹

¹Department of Neurology and Psychiatry, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Hubei Key Laboratory of Neural Injury and Functional Reconstruction, Huazhong University of Science and Technology, Wuhan 430030, China.

Journal of psychiatric research

|March 14, 2025

概括

大型抑郁症 (MDD) 与心率变化 (HRV) 的日中节律改变有关. 降低夜间HRV和特定的节律参数可以作为MDD的新生物标志物.

科学领域:

神经科学是一个神经科学.
心脏病学心脏病学
精神病学是一个精神病学.

背景情况:

大型抑郁症 (MDD) 与心率变化 (HRV) 的减少有关.
MDD与HRV的昼夜节律调节 (CRM) 之间的联系仍然不清楚.
抑郁症扰乱昼夜节律,这表明HRV波动可能揭示MDD的自主神经系统 (ANS) 变化.

研究的目的:

探索HRV和MDD的CRM之间的关系.
使用机器学习 (ML) 识别MDD的潜在生物标志物.

主要方法:

165名MDD患者和60名健康对照 (HCs) 接受了24小时的霍尔特心电图监测.
HRV昼夜节律用等号回归量化;ML模型用于分类.
在抗抑郁药治疗之前评估的心理尺度.

主要成果:

与HC相比,MDD患者的日间HRV降低,特别是在晚上,HRV昼夜节律的M参数降低.
抑郁症的严重程度与HRV参数相关 (RMSSD,PNN50,HF负;LF/HF比率为正).
梯度增强机 (GBM) 模型在分类MDD时实现了高精度 (0.823) 和AUC (0.868) .

结论:

在HRV中循环节律异常与MDD病理生理学有关.
人力资源价值观的CRM显示出作为MDD的新生物标志物的潜力.
机器学习模型,特别是GBM,可以有效地使用HRV特征对MDD进行分类.

关键词:

循环节律是指循环节律的节奏.心率变化心率的变化.机器学习机器学习大型抑郁症主要是抑郁症.

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