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

• URL: http://arxiv.org/abs/2205.06540v1
• Date: Fri, 13 May 2022 10:03:56 GMT
• Title: Accelerometry-based classification of circulatory states during out-of-hospital cardiac arrest
• Authors: Wolfgang J. Kern, Simon Orlob, Andreas Bohn, Wolfgang Toller, Jan Wnent, Jan-Thorsten Gr\"asner, Martin Holler
• Abstract summary: We developed a machine learning algorithm to automatically predict the circulatory state during cardiac arrest treatment. The algorithm was trained based on 917 cases from the German Resuscitation Registry.
• Score: 1.2109519547057512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objective: During cardiac arrest treatment, a reliable detection of spontaneous circulation, usually performed by manual pulse checks, is both vital for patient survival and practically challenging. Methods: We developed a machine learning algorithm to automatically predict the circulatory state during cardiac arrest treatment from 4-second-long snippets of accelerometry and electrocardiogram data from real-world defibrillator records. The algorithm was trained based on 917 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 14 features, which partially reflect the correlation between accelerometry and electrocardiogram data. Results: On a test data set, the proposed algorithm exhibits an accuracy of 94.4 (93.6, 95.2)%, a sensitivity of 95.0 (93.9, 96.1)%, and a specificity of 93.9 (92.7, 95.1)%. Conclusion and significance: In application, the 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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