title: Detrended fluctuation analysis predicts successful defibrillation for out-of-hospital ventricular fibrillation cardiac arrest.
abstract: OBJECTIVE
  Repeated failed shocks for ventricular fibrillation (VF) in out-of-hospital cardiac arrest (OOHCA) can worsen the outcome. It is very important to rapidly distinguish between early and late VF. We hypothesised that VF waveform analysis based on detrended fluctuation analysis (DFA) can help predict successful defibrillation.
  
  
  METHODS
  Electrocardiogram (ECG) recordings of VF signals from automated external defibrillators (AEDs) were obtained for subjects with OOHCA in Taipei city. To examine the time effect on DFA, we also analysed VF signals in subjects who experienced sudden cardiac death during Holter study from PhysioNet, a publicly accessible database. Waveform parameters including root-mean-squared (RMS) amplitude, mean amplitude, amplitude spectrum analysis (AMSA), frequency analysis as well as fractal measurements including scaling exponent (SE) and DFA were calculated. A defibrillation was regarded as successful when VF was converted to an organised rhythm within 5s after each defibrillation.
  
  
  RESULTS
  A total of 155 OOHCA subjects (37 successful and 118 unsuccessful defibrillations) with VF were included for analysis. Among the VF waveform parameters, only AMSA (7.61+/-3.30 vs. 6.30+/-3.13, P=0.028) and DFAalpha2 (0.38+/-0.24 vs. 0.49+/-0.24, P=0.013) showed significant difference between subjects with successful and unsuccessful defibrillation. The area under the curves (AUCs) for AMSA and DFAalpha2 was 0.63 (95% confidence interval (CI)=0.52-0.73) and 0.65 (95% CI=0.54-0.75), respectively. Among the waveform parameters, only DFAalpha2, SE and dominant frequency showed significant time effect.
  
  
  CONCLUSIONS
  The VF waveform analysis based on DFA could help predict first-shock defibrillation success in patients with OOHCA. The clinical utility of the approach deserves further investigation.
authors.affiliation: Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
authors.forename: Lian-Yu
authors.initials: LY
authors.lastname: Lin
authors.forename: Men-Tzung
authors.initials: MT
authors.lastname: Lo
authors.forename: Patrick Chow-In
authors.initials: PC
authors.lastname: Ko
authors.forename: Chen
authors.initials: C
authors.lastname: Lin
authors.forename: Wen-Chu
authors.initials: WC
authors.lastname: Chiang
authors.forename: Yen-Bin
authors.initials: YB
authors.lastname: Liu
authors.forename: Kun
authors.initials: K
authors.lastname: Hu
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: D003627:Data Interpretation, Statistical; D016757:Death, Sudden, Cardiac; D047548:Defibrillators; D004554:Electric Countershock; D015716:Electrocardiography, Ambulatory; D004569:Electroencephalography; D017709:Fractals; D006323:Heart Arrest / Q000209:etiology / Q000628:therapy*; D006801:Humans; D011237:Predictive Value of Tests; D012372:ROC Curve; D013997:Time Factors; D016896:Treatment Outcome; D014693:Ventricular Fibrillation / Q000150:complications / Q000503:physiopathology* / Q000628:therapy*