Objective: Exploit accelerometry data for an automatic, reliable, and prompt detection of spontaneous circulation during cardiac arrest, as this is both vital for patient survival and practically challenging. Methods: We developed a machine learning algorithm to automatically predict the circulatory state during cardiopulmonary resuscitation from 4-second-long snippets of accelerometry and electrocardiogram (ECG) data from pauses of chest compressions of real-world defibrillator records. The algorithm was trained based on 422 cases from the German Resuscitation Registry, for which ground truth labels were created by a manual annotation of physicians. It uses a kernelized Support Vector Machine classifier based on 49 features, which partially reflect the correlation between accelerometry and electrocardiogram data. Results: Evaluating 50 different test-training data splits, the proposed algorithm exhibits a balanced accuracy of 81.2%, a sensitivity of 80.6%, and a specificity of 81.8%, whereas using only ECG leads to a balanced accuracy of 76.5%, a sensitivity of 80.2%, and a specificity of 72.8%. Conclusion: The first method employing accelerometry for pulse/no-pulse decision yields a significant increase in performance compared to single ECG-signal usage. Significance: This shows that accelerometry provides relevant information for pulse/no-pulse decisions. In application, such an algorithm may be used to simplify retrospective annotation for quality management and, moreover, to support clinicians to assess circulatory state during cardiac arrest treatment.

翻译：目的：利用加速度测量数据实现心脏骤停期间自主循环的自动、可靠且快速检测，这对患者生存至关重要，但在实践中极具挑战性。方法：我们开发了一种机器学习算法，基于真实除颤器记录中胸外按压暂停时段的4秒加速度测量与心电图数据片段，自动预测心肺复苏期间的循环状态。该算法基于德国心脏骤停登记处的422例病例进行训练，其中金标准标签由医生手动标注。算法采用基于49项特征的核支持向量机分类器，部分特征反映了加速度测量与心电图数据之间的相关性。结果：在对50种不同测试-训练数据划分的评估中，所提算法展现出81.2%的平衡准确率、80.6%的灵敏度和81.8%的特异性；而仅使用心电图时，平衡准确率为76.5%，灵敏度为80.2%，特异性为72.8%。结论：首次采用加速度测量进行脉搏/无脉搏决策的方法，相比单独使用心电图信号显著提升了性能。意义：这表明加速度测量为脉搏/无脉搏决策提供了相关信息。实际应用中，此类算法可用于简化回顾性标注以进行质量管控，并可辅助临床医生在心脏骤停治疗期间评估循环状态。