title: A new method to estimate the amplitude spectrum analysis of ventricular fibrillation during cardiopulmonary resuscitation.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  The new algorithm could efficiently filter the CPR-related artifacts of the VF ECG and preserve the shockability index of the original VF waveform.
authors.affiliation: Research Center for Adaptive Data Analysis & Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taoyuan, Taiwan.
authors.forename: Men-Tzung
authors.initials: MT
authors.lastname: Lo
authors.forename: Lian-Yu
authors.initials: LY
authors.lastname: Lin
authors.forename: Wan-Hsin
authors.initials: WH
authors.lastname: Hsieh
authors.forename: Patrick Chow-In
authors.initials: PC
authors.lastname: Ko
authors.forename: Yen-Bin
authors.initials: YB
authors.lastname: Liu
authors.forename: Chen
authors.initials: C
authors.lastname: Lin
authors.forename: Yi-Chung
authors.initials: YC
authors.lastname: Chang
authors.forename: Cheng-Yen
authors.initials: CY
authors.lastname: Wang
authors.forename: Vincent Hsu-Wen
authors.initials: VH
authors.lastname: Young
authors.forename: Wen-Chu
authors.initials: WC
authors.lastname: Chiang
authors.forename: Jiunn-Lee
authors.initials: JL
authors.lastname: Lin
authors.forename: Wen-Jone
authors.initials: WJ
authors.lastname: Chen
authors.forename: Matthew Huei-Ming
authors.initials: MH
authors.lastname: Ma
mesh_terms: D000465:Algorithms; D016477:Artifacts; D016887:Cardiopulmonary Resuscitation / Q000379:methods*; D004562:Electrocardiography; D006801:Humans; D014693:Ventricular Fibrillation / Q000503:physiopathology*
keywords: Amplitude spectrum analysis; Automated external defibrillator; Empirical mode decomposition; Least mean square; Ventricular fibrillation