Detrended fluctuation analysis predicts successful defibrillation for out-of-hospital ventricular fibrillation cardiac arrest

Lian Yu Lin, Men Tzung Lo, Patrick Chow In Ko, Chen Lin, Wen Chu Chiang, Yen Bin Liu, Kun Hu, Jiunn Lee Lin, Wen Jone Chen, Matthew Huei Ming Ma

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

Aims: 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 5. s 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 DFAα2 (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 DFAα2 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 DFAα2, 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.

Original languageEnglish
Pages (from-to)297-301
Number of pages5
JournalResuscitation
Volume81
Issue number3
DOIs
Publication statusPublished - Mar 1 2010
Externally publishedYes

Fingerprint

Ventricular Fibrillation
Heart Arrest
Out-of-Hospital Cardiac Arrest
Spectrum Analysis
Shock
Confidence Intervals
Fractals
Defibrillators
Sudden Cardiac Death
Area Under Curve
Electrocardiography
Databases

Keywords

  • Automated external defibrillator
  • Defibrillation
  • Electrocardiography
  • Fractal
  • Ventricular fibrillation

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Emergency
  • Emergency Medicine

Cite this

Detrended fluctuation analysis predicts successful defibrillation for out-of-hospital ventricular fibrillation cardiac arrest. / Lin, Lian Yu; Lo, Men Tzung; Ko, Patrick Chow In; Lin, Chen; Chiang, Wen Chu; Liu, Yen Bin; Hu, Kun; Lin, Jiunn Lee; Chen, Wen Jone; Ma, Matthew Huei Ming.

In: Resuscitation, Vol. 81, No. 3, 01.03.2010, p. 297-301.

Research output: Contribution to journalArticle

Lin, Lian Yu ; Lo, Men Tzung ; Ko, Patrick Chow In ; Lin, Chen ; Chiang, Wen Chu ; Liu, Yen Bin ; Hu, Kun ; Lin, Jiunn Lee ; Chen, Wen Jone ; Ma, Matthew Huei Ming. / Detrended fluctuation analysis predicts successful defibrillation for out-of-hospital ventricular fibrillation cardiac arrest. In: Resuscitation. 2010 ; Vol. 81, No. 3. pp. 297-301.
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abstract = "Aims: 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 5. s 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 DFAα2 (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 DFAα2 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 DFAα2, 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.",
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AU - Lin, Chen

AU - Chiang, Wen Chu

AU - Liu, Yen Bin

AU - Hu, Kun

AU - Lin, Jiunn Lee

AU - Chen, Wen Jone

AU - Ma, Matthew Huei Ming

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N2 - Aims: 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 5. s 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 DFAα2 (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 DFAα2 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 DFAα2, 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.

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