IEEE open journal of the Computer Society · 2026
Cascade Skip-Connection BiLSTM Autoencoder for CPR Artifact Removal Prior to AED Shock Advisory.
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Lim Jaechan, Hicks David, Valentine Matt, Chon Ki H
Аннотация
BACKGROUND: Current automated external defibrillators require pauses in cardiopulmonary resuscitation (CPR) for reliable ECG rhythm analysis, reducing chest compression fraction and delaying defibrillation.
METHODS: We propose a skip-connection BiLSTM autoencoder (SBAE) that removes CPR artifacts directly from one-dimensional ECG signals without auxiliary reference signals or time-frequency transformation. A cascade architecture employs a balanced model for initial denoising followed by biased models whose reconstruction is optimized toward one rhythm class. A conservative routing strategy labels cases where the two stages disagree as indeterminate. Performance was evaluated through 10-fold cross-validation with artifact-level data separation using an FDA-approved shock advisory algorithm.
RESULTS: The cascade SBAE achieved 97.51 ± 1.91% sensitivity for ventricular fibrillation, 94.58 ± 3.39% for ventricular tachycardia, 99.30 ± 0.28% specificity for normal sinus rhythm, 98.02 ± 3.02% for asystole, and 97.21 ± 1.05% for other non-shockable rhythms, exceeding American Heart Association thresholds for all categories. The indeterminate rate was 3.63 ± 1.10%.
CONCLUSION: The proposed framework provides a compact, reference-free solution for shock advisory decision support in automated external defibrillators. Deployment on embedded AED hardware would require additional model optimization and platform-specific engineering.
METHODS: We propose a skip-connection BiLSTM autoencoder (SBAE) that removes CPR artifacts directly from one-dimensional ECG signals without auxiliary reference signals or time-frequency transformation. A cascade architecture employs a balanced model for initial denoising followed by biased models whose reconstruction is optimized toward one rhythm class. A conservative routing strategy labels cases where the two stages disagree as indeterminate. Performance was evaluated through 10-fold cross-validation with artifact-level data separation using an FDA-approved shock advisory algorithm.
RESULTS: The cascade SBAE achieved 97.51 ± 1.91% sensitivity for ventricular fibrillation, 94.58 ± 3.39% for ventricular tachycardia, 99.30 ± 0.28% specificity for normal sinus rhythm, 98.02 ± 3.02% for asystole, and 97.21 ± 1.05% for other non-shockable rhythms, exceeding American Heart Association thresholds for all categories. The indeterminate rate was 3.63 ± 1.10%.
CONCLUSION: The proposed framework provides a compact, reference-free solution for shock advisory decision support in automated external defibrillators. Deployment on embedded AED hardware would require additional model optimization and platform-specific engineering.