A new method to estimate the amplitude spectrum analysis of ventricular fibrillation during cardiopulmonary resuscitation

RESUSCITATION

卷:84

Article

Lo, Men-Tzung¹  Lin, Lian-Yu^2,3  Hsieh, Wan-Hsin^1,4  Ko, Patrick Chow-In⁵  Liu, Yen-Bin^2,3  Lin, Chen^1,6  Chang, Yi-Chung^1,7  Wang, Cheng-Yen¹  Young, Vincent Hsu-Wen¹  Chiang, Wen-Chu⁵  Lin, Jiunn-Lee^2,3  Chen, Wen-Jone⁵  Ma, Matthew Huei-Ming⁵ 

[1] Natl Cent Univ, Res Ctr Adapt Data Anal, Tao Yuan, Taiwan

[2] Natl Cent Univ, Ctr Dynam Biomarkers & Translat Med, Tao Yuan, Taiwan

[3] Natl Taiwan Univ Hosp, Dept Internal Med, Taipei 100, Taiwan

[4] Harvard Univ, Sch Med, Brigham & Womens Hosp, Med Biodynam Program,Div Sleep Med, Boston, MA 02115 USA

[5] Natl Taiwan Univ Hosp, Dept Emergency Med, Taipei, Taiwan

[6] Stanford Univ, Dept Psychiat & Behav Sci, Sch Med, Palo Alto, CA 94305 USA

[7] Natl Taiwan Univ, Grad Inst Commun Engn, Taipei 10764, Taiwan

关键词: Ventricular fibrillation;    Automated external defibrillator;    Empirical mode decomposition;    Least mean square;    Amplitude spectrum analysis;   

DOI  :  10.1016/j.resuscitation.2013.07.004

来源: Elsevier

【 摘 要 】

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. (C) 2013 Elsevier Ireland Ltd. All rights reserved.

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