睡眠期间的周期性腿部运动与REM睡眠行为障碍相关:一项机器学习研究
Maria Salsone1,2, Basilio Vescio3,4, Andrea Quattrone5
1Institute of Molecular Bioimaging and Physiology, National Research Council, 20054 Segrate, Italy.
Diagnostics (Basel, Switzerland)
|February 24, 2024
概括
机器学习准确地区分异常REM睡眠行为障碍 (iRBD) 患者和没有周期性腿部运动 (PLMS) 的患者. 这种人工智能方法支持使用心率变化数据诊断临床上类似的iRBD表型.
科学领域:
- 神经学 神经学
- 睡眠医学 睡眠医学
- 人工智能的人工智能
背景情况:
- 异常REM睡眠行为障碍 (iRBD) 患者经常表现出周期性腿部运动 (PLMS),使临床诊断复杂化.
- 区分iRBD表型与没有PLMS是具有挑战性的,没有多睡眠学.
研究的目的:
- 开发一种新的机器学习 (ML) 方法来区分基于心率变化 (HRV) 的iRBD表型.
主要方法:
- 收集了42名iRBD患者的HRV数据 (23名有PLMS,19名没有PLMS).
- 在HRV数据上训练了ML模型 (逻辑回归,支持向量机,随机森林,极端梯度提升).
- 使用Leave-One-Out交叉验证评估分类性能.
主要成果:
- 随机森林模型实现了最高的精度 (86%),灵敏度 (96%) 和特异性 (74%).
- 支持向量机和极端梯度增强模型也表现出良好的性能.
- 后勤回归显示分类准确度较低 (71%).
结论:
- 基于HRV,ML算法可以有效地区分iRBD表型.
- 人工智能在支持诊断临床上无法区分的iRBD亚型方面表现有前途.
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