Load Distributing Band (LDB) mechanical chest compression devices are used to treat out-of-hospital cardiac arrest (OHCA) patients. The artefacts that LDB chest compressions induce in the ECG impede a reliable shock/no-shock diagnosis, resulting in compression interruptions to analyze the ECG. The aim of this study was to design a deep learning algorithm to accurately detect shockable rhythms with concurrent LDB compressions. The dataset was comprised of 780 shockable and 2644 nonshockable rhythms from 242 OHCA patients treated with the LDB device. Underlying rhythms were annotated by expert reviewers in artefact-free intervals. The method consisted of two stages: a Recursive Least Squares (RLS) filter to remove LDB compression artefacts and a shock/no-shock decision algorithm based on CNNs. Shock/no-shock diagnoses were compared with the rhythm annotations to obtain the sensitivity (Se) and specificity (Sp) of the method. The median Se, Sp were 92.2%, 96.6%, respectively. The proposed algorithm met the American Heart Association's (AHA) requirements for rhythm analysis.