Objective. Cardiac arrhythmias are a leading cause of mortality worldwide. Wearable devices based on photoplethysmography give the opportunity to screen large populations, hence allowing for an earlier detection of pathological rhythms that might reduce the risks of complications and medical costs. While most of beat detection algorithms have been evaluated on normal sinus rhythm or atrial fibrillation recordings, the performance of these algorithms in patients with other cardiac arrhythmias, such as ventricular tachycardia or bigeminy, remain unknown to date. Approach. The PPG-beats open-source framework, developed by Charlton and colleagues, evaluates the performance of the beat detectors named QPPG, MSPTD and ABD among others. We applied the PPG-beats framework on two newly acquired datasets, one containing seven different types of cardiac arrhythmia in hospital settings, and another dataset including two cardiac arrhythmias in ambulatory settings. Main Results. In a clinical setting, the QPPG beat detector performed best on atrial fibrillation (with a median F 1 score of 94.4%), atrial flutter (95.2%), atrial tachycardia (87.0%), sinus rhythm (97.7%), ventricular tachycardia (83.9%) and was ranked 2nd for bigeminy (75.7%) behind ABD detector (76.1%). In an ambulatory setting, the MSPTD beat detector performed best on normal sinus rhythm (94.6%), and the QPPG detector on atrial fibrillation (91.6%) and bigeminy (80.0%). Significance. Overall, the PPG beat detectors QPPG, MSPTD and ABD consistently achieved higher performances than other detectors. However, the detection of beats from wrist-PPG signals is compromised in presence of bigeminy or ventricular tachycardia.