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

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 F1-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.