用U-net精准分割心肺复苏时的二氧化碳波形，提升临床监测可靠性。
Robust Segmentation of CPR-Induced Capnogram Using U-net: Overcoming Challenges with Deep Learning
- 基于U-net架构，自动区分心肺复苏中的吸气与非吸气阶段。
- 分割F1得分98%，通气检测准确率96%，优于现有方法4个百分点。
- 对低幅值信号和干扰有强鲁棒性，适合急诊临床实时应用。
目的：心肺复苏（CPR）期间准确分割二氧化碳波形对患者监测和气道管理至关重要。本研究旨在开发一种基于U-net架构的鲁棒算法，将波形分为吸气与非吸气阶段，并在存在CPR干扰的情况下验证其优于现有最先进（SoA）方法。材料与方法：使用来自1587名患者的24,354段一分钟波形数据进行训练与评估，采用患者级10折交叉验证。提取五项特征用于聚类分析，以评估不同信号特征下的性能表现。评价指标包括分段级与通气级指标，如通气频率与呼气末二氧化碳（end-tidal-CO₂）值。结果：所提U-net算法在分段任务上达到98% F1得分，通气检测达96%准确率，较现有方法提升4个百分点。呼气末二氧化碳与通气频率的均方根误差分别为1.9 mmHg和1.1次/分钟。详细性能分析显示该算法对CPR干扰和低幅值信号具有强鲁棒性，聚类分析进一步证明其在多种信号特性下表现一致。结论：该U-net分割算法显著提升了CPR期间二氧化碳波形分析的准确性，其在吸气相与通气事件检测上的优异表现，为临床应用提供了可靠工具，有望改善心脏骤停患者预后。
原文摘要 · Abstract (English)
Objective: The accurate segmentation of capnograms during cardiopulmonary resuscitation (CPR) is essential for effective patient monitoring and advanced airway management. This study aims to develop a robust algorithm using a U-net architecture to segment capnograms into inhalation and non-inhalation phases, and to demonstrate its superiority over state-of-the-art (SoA) methods in the presence of CPR-induced artifacts. Materials and methods: A total of 24354 segments of one minute extracted from 1587 patients were used to train and evaluate the model. The proposed U-net architecture was tested using patient-wise 10-fold cross-validation. A set of five features was extracted for clustering analysis to evaluate the algorithm performance across different signal characteristics and contexts. The evaluation metrics included segmentation-level and ventilation-level metrics, including ventilation rate and end-tidal-CO$_2$ values. Results: The proposed U-net based algorithm achieved an F1-score of 98% for segmentation and 96% for ventilation detection, outperforming existing SoA methods by 4 points. The root mean square error for end-tidal-CO$_2$ and ventilation rate were 1.9 mmHg and 1.1 breaths per minute, respectively. Detailed performance metrics highlighted the algorithm's robustness against CPR-induced interferences and low amplitude signals. Clustering analysis further demonstrated consistent performance across various signal characteristics. Conclusion: The proposed U-net based segmentation algorithm improves the accuracy of capnogram analysis during CPR. Its enhanced performance in detecting inhalation phases and ventilation events offers a reliable tool for clinical applications, potentially improving patient outcomes during cardiac arrest.
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