An Enhanced Adaptive Filtering Method for Suppressing Cardiopulmonary Resuscitation Artifact
Cardiopulmonary resuscitation (CPR) must be interrupted for reliable rhythm analysis in current automatic external defibrillators because of artifacts produced by chest compressions. However, interruptions in CPR adversely affect the restoration of spontaneous circulation and survival. Suppressing CPR artifacts by digital signal processing techniques is a promising method to enable rhythm analysis during chest compressions, which would eliminate CPR interruptions for rhythm analysis. Although numerous methods have been developed to suppress CPR artifacts, the accuracy of rhythm analysis is still inadequate due to the residual artifact components in the filtered signal. This study proposes an enhanced adaptive filtering method to suppress CPR artifacts. A total of 183 shockable and 453 nonshockable segments of ECG signal, together with CPR-related reference signal, were extracted from 233 out of hospital cardiac arrest patients. The method was optimized on a training set with 85 shockable and 211 nonshockable segments, and evaluated on a testing set with 98 shockable and 242 nonshockable segments. Compared with artifact corrupted ECG signals, the signal-to-noise ratio (SNR) increased from −9.8 ± 12.5 to 11.2 ± 11.8 dB, and the accuracy was improved from 74.1% to 92.0% after filtering with the proposed method. Compared with the traditional adaptive filter, the SNR was improved by 1.7 dB and the accuracy was improved by 5.6 points. These results indicated that the proposed method could effectively suppress the chest compression related artifacts and improve the accuracy of rhythm analysis during uninterrupted CPR.
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