Removing cardiopulmonary resuscitation (CPR) related artefacts from human ventricular fibrillation (VF) ECG signals would provide the possibility to continuously detect rhythm changes and estimate the probability of defibrillation success. This would avoid "hands-off" analysis times which diminish the cardiac perfusion and thus deteriorate the chance for a successful defibrillation attempt. Our approach consists in representing 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. The residuals of the Kalman estimation can be identified with the CPR-filtered ECG signal. Preliminary results using only a small pool of human VF and animal a systole CPR data show that the seasonal model is not as effective as models using reference signals, but it might be useful in combination with them.
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