Wearable devices based on photoplethysmography (PPG) allow for the screening of large populations at risk of cardiovascular disease. While PPG has shown the ability to discriminate atrial fibrillation (AF)-the most common cardiac arrhythmia (CA)-versus normal sinus rhythm, it is not clear whether such AF detectors are efficient in presence of CAs other than AF. We propose to apply a simple recurrent neural network (RNN) on a newly acquired dataset containing eight different types of CAs. The classifier takes sequences of inter-beat intervals (IBIs) as input and discriminates between normal and abnormal rhythm. The RNN achieved 84% accuracy in detecting abnormal rhythms. Some CAs were well detected (AF: 99.6%; atrial tachycardia: 100%), whereas other CAs were more difficult to detect (atrial flutter: 65.4%; bigeminy: 72.4%; ventricular tachycardia 80%). This study shows the potential of PPG technology to detect not only AF but also other types of CA. It highlights the strengths and weaknesses of IBI-based detection of abnormal rhythms and paves the way towards continuous monitoring of CAs in everyday life.