Key result
The LMS adaptive filter algorithm improved the sensitivity of shockable rhythm detection during CPR from 3.2% to 96.8% and specificity from 66.0% to 98.7%.
Why the study?
Does a CPR artifact removal algorithm using an LMS filter improve the diagnostic accuracy of ECG rhythms during CPR in a porcine model?
Does a CPR artifact removal algorithm using an LMS filter improve the diagnostic accuracy of ECG rhythms during CPR in a porcine model?
Absolute Event Rate: 96.8% vs 3.2%
An LMS adaptive filter effectively removes CPR artifacts from ECG signals in a porcine model, potentially enabling continuous chest compressions during AED rhythm analysis.
This study proposes an algorithm for removal of CPR artifact in order that automated external defibrillator (AED) can effectively diagnose ECG rhythm during cardiopulmonary resuscitation (CPR). Current AED required to interrupt chest compression for reliable rhythm analysis to avoid the effect of artifacts produced by CPR. However even temporarily interruption of chest compression during CPR adversely affects the probability of restoration of spontaneous circulation (ROSC) and survival after the delivery of the shock. Therefore, we proposed a method for removal of CPR artifacts using least mean square (LMS) filter. The removal of the CPR artifacts would enable compressions to continue during AED rhythm analysis, thereby increasing the likelihood of resuscitation success. It was tested on 31 segments of shockable and 300 segments of non-shockable ECG signals recorded from three pigs during CPR. In the result, sensitivity (Se) and specificity (Sp) analysis on the test segments showed values of Se = 3.2%, Sp = 66.0% and Se = 96.8%, Sp = 98.7% in the case of unfiltered and filtered signals during CPR. In conclusion, it was shown that the proposed method can be a useful tool to exactly diagnose the ECG rhythm during the CPR.
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Lim et al. (2016) studied Cardiac arrest (simulated) (n=3). LMS adaptive filter vs. Unfiltered ECG signals was evaluated on Sensitivity of shockable rhythm detection during CPR. The LMS adaptive filter algorithm improved the sensitivity of shockable rhythm detection during CPR from 3.2% to 96.8% and specificity from 66.0% to 98.7%.
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