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

Rheinberger, Klaus; Baubin, Michael; Unterkofler, Karl; Amann, Anton

doi:10.1109/cic.2005.1588161

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…The first aspect of the studies concerns the sets of signals used in the training/testing process. Usually materials are sets of noise-free ECG signals mixed with CPR artefacts obtained from real OHCAs, 2,3,6,11,26 or even recordings of human VF/VT mixed with CPR artefacts collected from anesthetized pigs during asystole. 1,22,30 The second aspect concerns the methods, which are all focused on filtering of the CPR artefact from ECG.…”

Section: Introductionmentioning

confidence: 99%

Shock Advisory System for Heart Rhythm Analysis During Cardiopulmonary Resuscitation Using a Single ECG Input of Automated External Defibrillators

et al. 2010

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Minimum ''hands-off'' intervals during cardiopulmonary resuscitation (CPR) are required to improve the success rate of defibrillation. In support of such life-saving practice, a shock advisory system (SAS) for automatic analysis of the electrocardiogram (ECG) contaminated by chest compression (CC) artefacts is presented. Ease of use for the automated external defibrillators (AEDs) is aimed and therefore only processing of ECG from usual defibrillation pads is required. The proposed SAS relies on assessment of outstanding components of ECG rhythms and CC artefacts in the time and frequency domain. For this purpose, three criteria are introduced to derive quantitative measures of band-pass filtered CC-contaminated ECGs, combined with three more criteria for frequency-band evaluation of reconstructed ECGs (rECG). The rECGs are derived by specific techniques for CC waves similarity assessment and are reproducing to some extent the underlying ECG rhythms. The rhythm classifier embedded in SAS takes a probabilistic decision designed by statistics on the training dataset. Both training and testing are fully performed on real CC-contaminated strips of 10 s extracted from human ECGs of out-ofhospital cardiac arrest interventions. The testing is done on 172 shockable strips (ventricular fibrillations VF), 371 nonshockable strips (NR) and 330 asystoles (ASYS). The achieved sensitivity of 90.1% meets the AHA performance goal for noise-free VF (>90%). The specificity of 88.5% for NR and 83.3% for ASYS are comparable or even better than accuracy reported in literature. It is important to note that, the aim of this SAS is not to recommend shock delivery but to advice the rescuers to ''Continue CPR'' or to ''Stop CPR and Prepare for Shock'' thus minimizing ''hands-off'' intervals.

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Section: Introductionmentioning

confidence: 99%

Shock Advisory System for Heart Rhythm Analysis During Cardiopulmonary Resuscitation Using a Single ECG Input of Automated External Defibrillators

et al. 2010

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“…• Rheinberger et al [18] model the CPR-corrupted signal by a seasonal state-space model. This allows for a stochastically changing shape of the periodic signal and also copes with time-dependent periods.…”

Section: B Previous Workmentioning

confidence: 99%

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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“…Among these publications, 56 engineering and medical papers were used as references according to the criteria. Finally, 8 papers from ScienceDirect, Web of Science and IEEEXplore about Kalman filter [28,29], independent component analysis (ICA) [30], coherent line removal [31] and adaptive filter [25,[32][33][34] were described, compared and discussed according to different artifact removal strategies.…”

Section: Methods Of Literature Searchmentioning

confidence: 99%

“…Rheinberger et al [29] proposed a Kalman state-space method for removing CPR artifacts in ECG signals. This approach represented the CPR-corrupted signal by a seasonal state-space model, which allowed for a stochastically changing shape of the periodic signal and also coped with time dependent periods.…”

Section: Methods Using Only Ecg Signals 411 Kalman Filtermentioning

confidence: 99%

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A Review of the Performance of Artifact Filtering Algorithms for Cardiopulmonary Resuscitation

Gong

Chen

2013

Journal of Healthcare Engineering

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Various filtering strategies have been adopted and investigated to suppress the cardiopulmonary resuscitation (CPR) artifact. In this article, two types of artifact removal methods are reviewed: one is the method that removes CPR artifact using only ECG signals, and the other is the method with additional reference signals, such as acceleration, compression depth and transthoracic impedance. After filtering, the signal-to-noise ratio is improved from 0 dB to greater than 2.8 dB, the sensitivity is increased to > 90% as recommended by the American Heart Association, whereas the specificity was far from the recommended 95%, which is considered to be the major drawback of the available artifact removal methods. The overall performance of the adaptive filtering methods with additional reference signal outperforms the methods using only ECG signals. Further research should focus on the refinement of artifact filtering methods and the improvement of shock advice algorithms with the presence of CPR.

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

Abstract: Removing cardiopulmonary resuscitation (CPR)

Cited by 4 publications

References 16 publications

Shock Advisory System for Heart Rhythm Analysis During Cardiopulmonary Resuscitation Using a Single ECG Input of Automated External Defibrillators

Shock Advisory System for Heart Rhythm Analysis During Cardiopulmonary Resuscitation Using a Single ECG Input of Automated External Defibrillators

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

A Review of the Performance of Artifact Filtering Algorithms for Cardiopulmonary Resuscitation

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