用深度学习分析新生儿监护信号，精准识别早产儿呼吸暂停事件。
Deep learning-based detection of cessation of breathing in pre-term infants
- 基于阻抗气动图等常规监护信号，用深度学习检测呼吸暂停。
- 单模态阻抗信号模型准确率达88.0%，优于心电和血氧信号。
- 融合多信号模型性能略有提升，适合临床实时监测应用。
早产儿呼吸暂停表现为反复呼吸停止，目前在新生儿重症监护室（NICU）中仍难以通过常规生理信号可靠检测。现有床旁监护仪主要依赖呼吸频率和血氧饱和度阈值，常产生高误报率并漏检短时或不规则事件。利用常规采集的临床信号改进自动检测方法，可在不增加额外传感器的情况下提升对临床有意义事件的识别能力。本研究评估了基于深度学习的呼吸暂停相关呼吸停止（COBE）事件检测，使用来自24名早产儿约430小时的NICU记录，涵盖阻抗气动图（IP）、心电图（ECG）和光电容积脉搏波（PPG）信号。三位独立评审员标注了346个COBE事件和608个非COBE事件。比较了浅层卷积神经网络（CNN）、残差网络（ResNets）和ConvNeXt架构，结果表明信号模态比模型复杂度影响更大：仅使用IP的模型平衡准确率达86.8%-88.0%，优于基于ECG（62.6%-69.7%）和PPG（65.1%-66.4%）的替代信号。多模态融合带来小幅提升，最优模型（结合IP与PPG的ConvNeXt）在独立测试集上达到88.7%的平衡准确率和0.75的F1分数。研究证实，深度学习可有效利用常规监护信号检测COBE事件，并强调信号类型在数据受限的新生儿监测中的关键作用。
原文摘要 · Abstract (English)
Apnoea of prematurity is characterised by recurrent episodes of cessation of breathing and remains difficult to detect reliably using routinely monitored physiological signals in the Neonatal Intensive Care Unit (NICU). Existing bedside monitors rely primarily on respiratory rate and oxygen saturation thresholds, often generating high false-positive alarm rates and missing short or irregular events. Improving automated detection using routinely acquired clinical signals could enhance identification of clinically meaningful events without additional sensing hardware. We evaluated deep learning-based detection of apnoea-related Cessation Of BrEathing (COBE) events using impedance pneumography (IP), electrocardiography (ECG), and photoplethysmography (PPG) signals from approximately 430 hours of NICU recordings collected from 24 pre-term infants. Three independent reviewers annotated COBE events, producing a dataset of 346 COBE and 608 non-COBE events. We compared a shallow convolutional neural network (CNN), residual networks (ResNets), and a ConvNeXt architecture using an independent held-out test set. Across all architectures, detection performance was influenced more strongly by signal modality than by architectural complexity. Unimodal IP-based models achieved balanced accuracies of 86.8-88.0%, outperforming ECG-derived (62.6-69.7%) and PPG-derived (65.1-66.4%) respiratory surrogates. Multimodal fusion yielded modest improvements over IP alone. The best-performing model, a ConvNeXt architecture combining IP and PPG inputs, achieved 88.7% balanced accuracy and an F1 score of 0.75 on the independent test set. These findings demonstrate that deep learning models applied to routinely monitored NICU signals can reliably detect COBE events and highlight the importance of signal modality in data-constrained neonatal monitoring settings.
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