Ventricular fibrillation detection in ventricular fibrillation signals corrupted by cardiopulmonary resuscitation artifact

Ruiz, J.; Aramendi, Elisabete; Gauna, Sofía Ruiz de; Lazkano, A.; Leturiondo, L.A.; Gutiérrez, J.J.

doi:10.1109/cic.2003.1291130

Cited by 3 publications

(6 citation statements)

References 12 publications

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“…The mean rSNR values computed from our small pool of human artifact-free VF and porcine asystole CPR data are slightly better than the values reported by [16], [12]. These research groups, however, not only used different algorithms but also different data pools, such that the results are not truly comparable.…”

Section: Discussionmentioning

confidence: 45%

“…Ruiz et al [12], [13] also use adaptive Kalman filtering methods. They model the CPR artifact with a sinusoidal signal of known frequency and variable phase and amplitude, whereas our CPR signal model might be less restrictive.…”

Section: Discussionmentioning

confidence: 99%

“…• Ruiz et al [12], [13] use Kalman filters assuming that the CPR artifact as well as the VF signal can be modelled by sinusoidal functions of known angular frequencies. After adding human asystole ECGs containing CPR artifacts to human artifact-free VF signals, they evaluated the signal-to-noise (SNR) improvements of their algorithm.…”

Section: B Previous Workmentioning

confidence: 99%

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Removal of CPR Artifacts From the Ventricular Fibrillation ECG by Adaptive Regression on Lagged Reference Signals

Rheinberger

Steinberger

Unterkofler

et al. 2008

IEEE Trans. Biomed. Eng.

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Section: Discussionmentioning

confidence: 45%

Section: Discussionmentioning

confidence: 99%

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Removal of CPR Artifacts From the Ventricular Fibrillation ECG by Adaptive Regression on Lagged Reference Signals

Rheinberger

Steinberger

Unterkofler

et al. 2008

IEEE Trans. Biomed. Eng.

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“…All of the resulting 49 signals have 5 seconds duration. After separation of the mixture by means of a removal algorithm the restored SNR [10,8] rSNR := 10 · log 10 V ar(signal) V ar(signal-estimation) reflects the mean squared estimation error of the reconstruction in comparison with the signal variance, c.f. original VF+CPR signal (thin), and estimated CPR part (bold) Figure 1.…”

Section: Data and Evaluation Methodsmentioning

confidence: 99%

“…In contrast to the large amount of literature about algorithms to detect and analyse VF signals [7,5], there are surprisingly only few and recent publications addressing the problem of removing CPR artefacts: Ruiz et al [8] use Kalman filters assuming that the CPR artefact as well as the VF signal can be modeled by sinusoidal functions of known angular frequencies. Klotz et al [9] propose a methodology based on time-frequency methods and local coherent line removal.…”

Section: Introductionmentioning

confidence: 99%

Removal of resuscitation artefacts from ventricular fibrillation ECG signals using kalman methods

Rheinberger

Baubin

Unterkofler

et al. 2005

Computers in Cardiology, 2005

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Reduction of CPR artifacts in the ventricular fibrillation ECG by coherent line removal

et al. 2010

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BackgroundInterruption of cardiopulmonary resuscitation (CPR) impairs the perfusion of the fibrillating heart, worsening the chance for successful defibrillation. Therefore ECG-analysis during ongoing chest compression could provide a considerable progress in comparison with standard analysis techniques working only during "hands-off" intervals.MethodsFor the reduction of CPR-related artifacts in ventricular fibrillation ECG we use a localized version of the coherent line removal algorithm developed by Sintes and Schutz. This method can be used for removal of periodic signals with sufficiently coupled harmonics, and can be adapted to specific situations by optimal choice of its parameters (e.g., the number of harmonics considered for analysis and reconstruction). Our testing was done with 14 different human ventricular fibrillation (VF) ECGs, whose fibrillation band lies in a frequency range of [1 Hz, 5 Hz]. The VF-ECGs were mixed with 12 different ECG-CPR-artifacts recorded in an animal experiment during asystole. The length of each of the ECG-data was chosen to be 20 sec, and testing was done for all 168 = 14 × 12 pairs of data. VF-to-CPR ratio was chosen as -20 dB, -15 dB, -10 dB, -5 dB, 0 dB, 5 dB and 10 dB. Here -20 dB corresponds to the highest level of CPR-artifacts.ResultsFor non-optimized coherent line removal based on signals with a VF-to-CPR ratio of -20 dB, -15 dB, -10 dB, -5 dB and 0 dB, the signal-to-noise gains (SNR-gains) were 9.3 ± 2.4 dB, 9.4 ± 2.4 dB, 9.5 ± 2.5 dB, 9.3 ± 2.5 dB and 8.0 ± 2.7 (mean ± std, n = 168), respectively. Characteristically, an original VF-to-CPR ratio of -10 dB, corresponds to a variance ratio var(VF):var(CPR) = 1:10. An improvement by 9.5 dB results in a restored VF-to-CPR ratio of -0.5 dB, corresponding to a variance ratio var(VF):var(CPR) = 1:1.1, the variance of the CPR in the signal being reduced by a factor of 8.9.DiscussionThe localized coherent line removal algorithm uses the information of a single ECG channel. In contrast to multi-channel algorithms, no additional information such as thorax impedance, blood pressure, or pressure exerted on the sternum during CPR is required. Predictors of defibrillation success such as mean and median frequency of VF-ECGs containing CPR-artifacts are prone to being governed by the harmonics of the artifacts. Reduction of CPR-artifacts is therefore necessary for determining reliable values for estimators of defibrillation success.ConclusionsThe localized coherent line removal algorithm reduces CPR-artifacts in VF-ECG, but does not eliminate them. Our SNR-improvements are in the same range as offered by multichannel methods of Rheinberger et al., Husoy et al. and Aase et al. The latter two authors dealt with different ventricular rhythms (VF and VT), whereas here we dealt with VF, only. Additional developments are necessary before the algorithm can be tested in real CPR situations.

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Ventricular fibrillation detection in ventricular fibrillation signals corrupted by cardiopulmonary resuscitation artifact

Abstract: Abstract

Cited by 3 publications

References 12 publications

Removal of CPR Artifacts From the Ventricular Fibrillation ECG by Adaptive Regression on Lagged Reference Signals

Removal of CPR Artifacts From the Ventricular Fibrillation ECG by Adaptive Regression on Lagged Reference Signals

Removal of resuscitation artefacts from ventricular fibrillation ECG signals using kalman methods

Reduction of CPR artifacts in the ventricular fibrillation ECG by coherent line removal

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