ALPEC：面向临床实践中基于机器学习的觉醒检测的综合评估框架与数据集
ALPEC: A Comprehensive Evaluation Framework and Dataset for Machine Learning-Based Arousal Detection in Clinical Practice
AI总结：
本文提出了ALPEC框架与CPS数据集，通过强调近似定位和精确事件计数，使基于机器学习的睡眠觉醒检测与临床标注实践对齐，并验证了多模态数据的优势，缩小了技术与临床需求的差距。
摘要：
检测睡眠中的觉醒对于诊断睡眠障碍至关重要。然而，在临床实践中应用机器学习（ML）受到基本问题的阻碍，主要是由于临床协议与ML方法之间的不匹配。临床医生通常只标注觉醒的开始，而ML方法则依赖于对开始和结束的标注。此外，目前没有针对临床需求量身定制的觉醒检测模型标准化评估方法。本研究通过引入一个新颖的后处理和评估框架来解决这些问题，该框架强调觉醒的近似定位和精确事件计数（ALPEC）。我们建议ML从业者专注于检测觉醒起始，以与临床实践保持一致。我们考察了这种转变对当前训练和评估方案的影响，并解决了简化与挑战。我们利用了一个新颖的综合多导睡眠图数据集（CPS），该数据集反映了上述临床标注约束，并包含了现有数据集中不存在的模态。我们随本文发布该数据集，展示了利用多模态数据进行觉醒起始检测的益处。我们的发现对将基于ML的觉醒检测整合到临床环境中做出了重要贡献，缩小了技术进步与临床需求之间的差距。
英文摘要：
Detecting arousals in sleep is essential for diagnosing sleep disorders. However, using Machine Learning (ML) in clinical practice is impeded by fundamental issues, primarily due to mismatches between clinical protocols and ML methods. Clinicians typically annotate only the onset of arousals, while ML methods rely on annotations for both the beginning and end. Additionally, there is no standardized evaluation methodology tailored to clinical needs for arousal detection models. This work addresses these issues by introducing a novel post-processing and evaluation framework emphasizing approximate localization and precise event count (ALPEC) of arousals. We recommend that ML practitioners focus on detecting arousal onsets, aligning with clinical practice. We examine the impact of this shift on current training and evaluation schemes, addressing simplifications and challenges. We utilize a novel comprehensive polysomnographic dataset (CPS) that reflects the aforementioned clinical annotation constraints and includes modalities not present in existing polysomnographic datasets. We release the dataset alongside this paper, demonstrating the benefits of leveraging multimodal data for arousal onset detection. Our findings significantly contribute to integrating ML-based arousal detection in clinical settings, reducing the gap between technological advancements and clinical needs.
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