发表机构
Yizhun Medical AI Co., Ltd.（医准医疗人工智能有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对睡眠纺锤波在脑电信号中占比小、检测难问题，提出SpindleFlexNet框架，基于自适应一维视网膜网络架构，经两个数据集训练和验证，该框架检测性能稳定、泛化性好，为睡眠研究提供实用工具。
AI 中文摘要
睡眠纺锤波是脑电图（EEG）波形中具有生理意义的生物医学信号，在睡眠中通常是低振幅事件。由于其在整体脑电信号中占比小，以往检测方法捕捉其起止点能力有限，处理多纺锤波场景缺乏灵活性。为此引入SpindleFlexNet，这是该领域首个应用基于深度学习的一维目标检测框架，采用自适应一维视网膜网络架构，包括一维锚点生成、匹配、回归及定制的一维损失函数。在两个公共数据集上分析，训练后在五折交叉验证中取得平均召回率、精确率和F1分数分别为0.61、0.76、0.67和0.58、0.80、0.67。该模型检测性能稳定、泛化性好，是睡眠研究实用工具，有临床自动纺锤波标记等潜在应用。
英文摘要
Sleep spindle is a physiologically significant biomedical signal in electroencephalographic (EEG) waveforms, which is typically a low-amplitude event in sleep. Due to the small signal ratio in the overall EEG, previous detection methods have limited capability to capture its start and end points and lack flexibility in handling multi-spindle scenarios. To address the gap, we address the problem from a new perspective and introduce SpindleFlexNet, the first framework in this field to apply deep learning-based one-dimensional object detection, leveraging an adapted one-dimensional RetinaNet architecture. The framework employs one-dimensional anchor generation, matching, and regression, along with a customized one-dimensional loss function. Analyses were conducted on two public datasets: the Montreal Archive of Sleep Studies and DREAMS, from which a total of 11,061 and 335 segments were obtained, respectively. When trained on these datasets, SpindleFlexNet achieved an average recall, precision, and F1-score of 0.61, 0.76, 0.67, and 0.58, 0.80, 0.67 in five-fold cross-validation. The model demonstrates stable detection performance and good generalization, making it a practical tool for sleep research. Potential applications include automated spindle labeling in clinical settings and as a reference for studies combining EEG with simultaneous functional magnetic resonance imaging.
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