AI Article Synopsis
- The study aimed to improve the analysis of ventricular fibrillation (VF) during CPR, as traditional methods may require stopping CPR, which can worsen patient outcomes.
- Researchers developed an algorithm that uses empirical mode decomposition and least square mean fitting to isolate and remove CPR artifacts from ECG readings, allowing for better preservation of the VF waveform.
- Analysis of 150 patients revealed that this new algorithm significantly improved the identification of useful VF signals while maintaining the predictive accuracy for successful defibrillation, compared to traditional corrupted ECG readings.
Article Abstract
Aims: Accurate ventricular fibrillation (VF) waveform analysis usually requires rescuers to discontinue cardiopulmonary resuscitation (CPR). However, prolonged "hands-off" time has a deleterious impact on the outcome. We developed a new filter technique that could clean the CPR artifacts and help preserve the shockability index of VF METHODS: We analyzed corrupted ECGs, which were constructed by randomly adding different scaled CPR artifacts to the VF waveforms. A newly developed algorithm was used to identify the CPR fluctuations. The algorithm contained two steps. First, decomposing the raw data by empirical mode decomposition (EMD) into several intrinsic mode fluctuations (IMFs) and combining the dominant IMFs to reconstruct a new signal. Second, calculating each CPR cycle frequency from the new signal and fitting the new signal to the original corrupted ECG by least square mean (LSM) method to derive the CPR artifacts. The estimated VF waveform was derived by subtraction of the CPR artifacts from the corrupted ECG. We then performed amplitude spectrum analysis (AMSA) for original VF, corrupted ECG and estimated VF.
Results: A total of 150 OHCA subjects with initial VF rhythm were included for analysis. Ten CPR artifacts signals were used to construct corrupted ECG. Even though the correlations of AMSA between the corrupted ECG vs. the original VF and the estimated VF vs. the original VF are all high (all p<0.001), the values of AMSA were obviously biased in corrupted ECG with wide limits of agreement in Bland-Altman mean-difference plot. ROC analysis of the AMSA in the prediction of defibrillation success showed that the new algorithm could preserve the cut-off AMSA value for CPR artifacts with power ratio to VF from 0 to 6 dB.
Conclusion: The new algorithm could efficiently filter the CPR-related artifacts of the VF ECG and preserve the shockability index of the original VF waveform.
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| http://dx.doi.org/10.1016/j.resuscitation.2013.07.004 | DOI Listing |
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