Cardiac arrhythmias present a significant global health concern. The advent of wearable devices utilizing photoplethysmography gives the opportunity to screen large populations, hence offering the potential for early detection of pathological rhythms and reducing risks of complications and associated medical costs. While most beat detection algorithms have been evaluated on normal sinus rhythm or atrial fibrillation recordings, their performance in patients with other cardiac arrhythmias remains unexplored to date. To address this gap, we leveraged the open-source framework PPG-beats, developed by Charlton and colleagues, to analyse a newly acquired dataset comprising seven distinct types of cardiac arrhythmia in hospital settings. Among the thirteen beat detectors evaluated, the QPPG detector performed best on atrial fibrillation (with a median F1 score of 94.4%), atrial flutter (95.2%), atrial tachycardia (87.0%), sinus rhythm (97.7%), ventricular tachycardia (83.9%) and was ranked second for bigeminy (75.7%) behind the ABD detector (76.1%). Overall, the QPPG beat detector achieved high performances and consistently outperformed other detectors. However, the detection of beats from wrist-PPG signals is compromised in the presence of bigeminy or ventricular tachycardia.