用机器学习自动识别小鼠睡眠状态，准确率达91.5%。
Automated Vigilance State Classification in Rodents Using Machine Learning and Feature Engineering
- 结合时域频域特征，提取脑电图的生理标志
- XGBoost模型准确率91.5%，优于所有基线方法
- 适合睡眠研究与慢性失眠干预开发人员使用
前临床睡眠研究受限于人工耗时的状态分类和评分者差异，影响通量与可重复性。本研究由神经预后团队提出自动化框架，对小型啮齿类动物脑电图（EEG）进行觉醒、慢波睡眠（SWS）和快速眼动睡眠（REM）三态分类。系统融合先进信号处理与机器学习，利用时频域工程特征，包括经典脑电波段（δ至γ）功率、最大最小距离等时间动态特征，以及跨频率耦合度量。这些特征捕捉了不同状态的神经生理特征：觉醒期高频去同步化、慢波睡眠期δ振荡、快动眼期特有爆发。在南卡罗来纳大学大数据健康科学中心2024年大赛中验证，所提XGBoost模型整体准确率达91.5%，精确率为86.8%，召回率为81.2%，F1得分为83.5%，优于所有基线方法。该方法为自动化睡眠状态分类带来关键进展，是加速睡眠科学研究及针对性干预手段开发的重要工具。公开代码资源（BDHSC）将显著推动领域发展。
原文摘要 · Abstract (English)
Preclinical sleep research remains constrained by labor intensive, manual vigilance state classification and inter rater variability, limiting throughput and reproducibility. This study presents an automated framework developed by Team Neural Prognosticators to classify electroencephalogram (EEG) recordings of small rodents into three critical vigilance states paradoxical sleep (REM), slow wave sleep (SWS), and wakefulness. The system integrates advanced signal processing with machine learning, leveraging engineered features from both time and frequency domains, including spectral power across canonical EEG bands (delta to gamma), temporal dynamics via Maximum-Minimum Distance, and cross-frequency coupling metrics. These features capture distinct neurophysiological signatures such as high frequency desynchronization during wakefulness, delta oscillations in SWS, and REM specific bursts. Validated during the 2024 Big Data Health Science Case Competition (University of South Carolina Big Data Health Science Center, 2024), our XGBoost model achieved 91.5% overall accuracy, 86.8% precision, 81.2% recall, and an F1 score of 83.5%, outperforming all baseline methods. Our approach represents a critical advancement in automated sleep state classification and a valuable tool for accelerating discoveries in sleep science and the development of targeted interventions for chronic sleep disorders. As a publicly available code (BDHSC) resource is set to contribute significantly to advancements.
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