Artificial Intelligence for Multiclass Rhythm Analysis for Out-of-Hospital Cardiac Arrest During Mechanical Cardiopulmonary Resuscitation

Iraia Isasi (), Xabier Jaureguibeitia, Erik Alonso, Andoni Elola, Elisabete Aramendi and Lars Wik
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Iraia Isasi: Department of Applied Mathematics, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
Xabier Jaureguibeitia: Department of Communications Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
Erik Alonso: Department of Applied Mathematics, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
Andoni Elola: Department of Electronic Technology, University of the Basque Country (UPV/EHU), 20600 Eibar, Spain
Elisabete Aramendi: Biocruces Bizkaia Health Research Institute, Cruces Plaza, 48903 Barakaldo, Spain
Lars Wik: Norwegian National Advisory Unit on Prehospital Emergency Medicine (NAKOS), Division of Prehospital Services, Oslo University Hospital, N-0424 Oslo, Norway

Mathematics, 2025, vol. 13, issue 8, 1-21

Abstract: Load distributing band (LDB) mechanical chest compression (CC) devices are used to treat out-of-hospital cardiac arrest (OHCA) patients. Mechanical CCs induce artifacts in the electrocardiogram (ECG) recorded by defibrillators, potentially leading to inaccurate cardiac rhythm analysis. A reliable analysis of the cardiac rhythm is essential for guiding resuscitation treatment and understanding, retrospectively, the patients’ response to treatment. The aim of this study was to design a deep learning (DL)-based framework for cardiac automatic multiclass rhythm classification in the presence of CC artifacts during OHCA. Concretely, an automatic multiclass cardiac rhythm classification was addressed to distinguish the following types of rhythms: shockable (Sh), asystole (AS), and organized (OR) rhythms. A total of 15,479 segments (2406 Sh, 5481 AS, and 7592 OR) were extracted from 2058 patients during LDB CCs, whereof 9666 were used to train the algorithms and 5813 to assess the performance. The proposed architecture consists of an adaptive filter for CC artifact suppression and a multiclass rhythm classifier. Two DL alternatives were considered for the multiclass classifier: convolutional neuronal networks (CNNs) and residual networks (ResNets). A traditional machine learning-based classifier, which incorporates the research conducted over the past two decades in ECG rhythm analysis using more than 90 state-of-the-art features, was used as a point of comparison. The unweighted mean of sensitivities, the unweighted mean of F 1 -Scores, and the accuracy of the best method (ResNets) were 88.3%, 88.3%, and 88.2%, respectively. These results highlight the potential of DL-based methods to provide accurate cardiac rhythm diagnoses without interrupting mechanical CC therapy.

Keywords: out-of-hospital cardiac arrest; cardiopulmonary resuscitation; mechanical chest compression device; chest compression artifacts; multiclass cardiac rhythm classification; artificial intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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