Human ECGs corrupted with real CPR artefacts in an animal model: Generating a database to evaluate and refine algorithms for eliminating CPR artefacts

• Published in: Resuscitation 2010-06, Vol.81 (6), p.730-736
• Main Authors: Granegger, M, Werther, T, Roehrich, M, Losert, U, Gilly, H
• Format: Article
• Language: English
• Subjects: Algorithms; Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy; Animals; Automated external defibrillator (AED); Biological and medical sciences; Cardiopulmonary Resuscitation; Cardiopulmonary resuscitation (CPR); CPR-artefact removal; Databases, Factual; Defibrillators; Electrocardiography; Emergency; Emergency and intensive cardiocirculatory care. Cardiogenic shock. Coronary intensive care; Emergency Medical Services; Humans; Intensive care medicine; Medical sciences; Porcine model; Swine
• ISSN: 0300-9572; 1873-1570
• DOI: 10.1016/j.resuscitation.2010.03.003

Abstract Aim For the analysis of ECG rhythms during ongoing CPR, single- or two-channel methods have been proposed to eliminate artefacts from the CPR-corrupted ECG. To refine, test and evaluate these algorithms with a realistic data set, we introduce an animal model with which we created an extended database of human ECGs with real CPR artefacts. Material and methods In a pig model real CPR-related artefacts were added to annotated human emergency ECGs. Via a special catheter placed in the oesophagus, ECG sequences (duration > 10 s) were fed in close to the dead pig's heart. The resulting surface potential was recorded on the thorax without and during ongoing chest compressions, which were monitored using a miniature force sensor. Results The animals served as a vehicle for human ECGs, making it possible to create a database in which 918 real human ECG sequences (437 shockable and 481 non-shockable) were corrupted with CPR-induced artefacts. The achieved signal-to-noise ratios (SNR) ranged from −17 to +15 dB, sensitivity was 93.5% and specificity was 50.51%. The fed-in ECG and the uncorrupted surface ECG correlated almost perfectly ( r = 0.926 ± 0.081; n = 918), indicating negligible signal distortion due to the dead pig itself. Conclusion As the generated database includes both the original and the corrupted ECG covering a wide range of SNRs as well as the compression force signal, it provides an extended data set to evaluate the reconstruction performance of CPR artefact-removal algorithms.