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arXiv · 2205.06540

Accelerometry-based classification of circulatory states during out-of-hospital cardiac arrest

Wolfgang J. Kern·Simon Orlob·Andreas Bohn·Wolfgang Toller·Jan Wnent·Jan-Thorsten Gräsner·Martin Holler
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Abstract

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.

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Wolfgang J. Kern, Simon Orlob, Andreas Bohn, Wolfgang Toller, Jan Wnent, Jan-Thorsten Gräsner, Martin Holler. 2023-02-10. Accelerometry-based classification of circulatory states during out-of-hospital cardiac arrest. https://doi.org/10.1109/tbme.2023.3242717

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