An ECG-based random forest classifier achieved an unweighted mean sensitivity of 80.2% for classifying out-of-hospital cardiac arrest rhythms, outperforming previous solutions by 2 points.
Does an automatic random forest classifier accurately annotate out-of-hospital cardiac arrest rhythms compared to expert clinician consensus?
An ECG-based random forest classifier can accurately annotate out-of-hospital cardiac arrest rhythms, potentially reducing the workload of manual annotation for large datasets.
Rhythm annotation of out-of-hospital cardiac episodes (OHCA) is key for a better understanding of the interplay between resuscitation therapy and OHCA patient outcome. OHCA rhythms are classified in five categories, asystole (AS), pulseless electrical activity (PEA), pulsed rhythms (PR), ventricular fibrillation (VF) and ventricular tachycardia (VT). Manual OHCA annotation by expert clinicians is onerous and time consuming, so there is a need for accurate and automatic OHCA rhythm annotation methods. For this study 852 OHCA episodes of patients treated with Automated External Defibrillators (AED) by the Emergency Medical Services of the Basque Country were analyzed. Six expert clinicians reviewed the electrocardiogram (ECG) of 4214 AED rhythm analyses and annotated the rhythm. Their consensus decision was used as ground truth. There were a total of 2418 AS, 294 PR, 1008 PEA, 472 VF and 22 VT. The ECG analysis intervals were extracted and used to develop an automatic rhythm annotator. Data was partitioned patient-wise into training (70%) and test (30%). Performance was evaluated in terms of per class sensitivity (Se) and F-score (F1). The unweighted mean of sensitivity (UMS) and F-score were used as global performance metrics. The classification method is composed of a feature extraction and denoising stage based on the stationary wavelet transform of the ECG, and on a random forest classifier. The best model presented a per rhythm Se/F1 of 95.8/95.7, 43.3/52.2, 85.3/81.3, 94.2/96.1, 81.9/72.2 for AS, PR, PEA, VF and VT, respectively. The UMS for the test set was 80.2%, 2-points above that of previous solutions. This method could be used to retrospectively annotate large OHCA datasets and ameliorate the workload of manual OHCA rhythm annotation.
Manibardo et al. (Mon,) conducted a other in Out-of-hospital cardiac arrest (OHCA) (n=852). ECG-based Random Forest Classifier vs. Manual OHCA annotation by expert clinicians was evaluated on Unweighted mean of sensitivity (UMS) and F-score for rhythm classification. An ECG-based random forest classifier achieved an unweighted mean sensitivity of 80.2% for classifying out-of-hospital cardiac arrest rhythms, outperforming previous solutions by 2 points.
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