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：利用加速度计数据实现心搏骤停期间自主循环的自动、可靠、快速检测，这对患者生存至关重要且具有实践挑战性。方法：我们开发了一种机器学习算法，通过真实除颤器记录中胸部按压暂停期的4秒加速度计与心电图数据片段，自动预测心肺复苏期间的循环状态。该算法基于德国复苏登记处的422例病例进行训练，其真实标签由医师人工标注生成。算法采用基于49个特征的核支持向量机分类器，其中部分特征反映加速度计与心电图数据的相关性。结果：在50组不同的测试-训练数据划分评估中，所提算法均衡准确率达81.2%，灵敏度80.6%，特异度81.8%；而仅使用心电图时均衡准确率为76.5%，灵敏度80.2%，特异度72.8%。结论：首个采用加速度计进行脉搏/无脉决策的方法较单一心电图信号显著提升性能。意义：研究表明加速度计能为脉搏/无脉决策提供关键信息。实际应用中，该算法可简化质量管理中的回顾性标注流程，并辅助临床医师在心搏骤停治疗中评估循环状态。