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基于自由生活条件下的睡眠心率的机器学习模型来分类月经周期阶段和检测排卵日
Hazuki Masuda1, Shima Okada2, Naruhiro Shiozawa3
1Graduate School of Science and Engineering, Ritsumeikan University Graduate School, Shiga, 5258577, Japan.
Computers in biology and medicine
|January 31, 2025
概括
一个新的心率功能,minHR,准确地跟踪月经周期和排卵,优于睡眠不规则的女性的传统方法. 这种预付款有助于个性化的女性健康管理.
科学领域:
- 生物医学工程 生物医学工程
- 妇女健康 技术 技术
- 生理监测 生理监测
背景情况:
- 精确的月经周期阶段分类和排卵检测对于妇女的健康至关重要,包括管理不孕症和与激素有关的疾病.
- 传统的基本体温 (BBT) 方法由于睡眠和环境变化而不可靠.
- 对于强大的月经周期跟踪,需要新的生理标记.
研究的目的:
- 为了引入和验证一个新的功能,心率在昼夜节律点 (minHR),为月经周期阶段分类和排卵预测.
- 为了比较基于minHR的模型与传统方法和基于日的模型的性能.
- 评估模型在不同睡眠模式的个体中的有效性.
主要方法:
- 开发了一个XGBoost机器学习模型,使用了40名健康女性在多个周期中的数据.
- 在自由生活条件下收集的数据,包括月经开始以来的天数,minHR和BBT等特征.
- 雇员嵌套离开-一个组-退出交叉验证,按睡眠变化分层参与者.
主要成果:
- 与单独使用日数相比,添加minHR显著改善了质阶段分类和排卵日检测.
- 在睡眠时间变化较高的参与者中,minHR模型的表现优于BBT模型.
- 在这个群体中,minHR模型将排卵日检测误差降低了2天 (p < 0.05),并改善了黄质期回忆.
结论:
- minHR功能为月经周期跟踪和排卵预测提供了一种强大而实用的方法.
- 这种方法对于睡眠时间变化很高的人来说尤其有益.
- 基于minHR的模型显示了个性化的女性健康管理和流行病学研究的巨大潜力.
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