Atrial fibrillation (AF) affects millions of people worldwide and needs to be diagnosed in its early stage to provide proper treatment. However, the numerous wearable devices available today are not yet able to discriminate AF episodes from other cardiac arrhythmias and merely detect normal vs abnormal rhythms.In this study we investigated the performance of a traditional classifier - designed to distinguish AF and sinus rhythm (SR) using inter-beat intervals (IBI) - when confronted with other - non-AF - arrhythmias. This classifier was challenged with data of 37 patients wearing an optical heart rate monitor device during catheter ablation procedures. We first analyzed the classification performance of pure AF vs SR and then gradually introduced non-AF arrhythmias in the time windows used for classification.We obtained a high classification performance (accuracy, sensitivity and specificity of 0.979, 1.000 and 0.966) for purely AF and SR. In contrast, when increasing the maximal possible number of non-AF arrhythmias to 50%, the performance decreased to an accuracy, sensitivity and specificity of 0.886, 0.998 and 0.853. While sinus tachycardia led to false positives the classification was not impaired by the presence of extrasystoles, bigeminy, bradycardia, frequent ectopic beats or atrial flutter.Our study quantifies to what extent a traditional IBI-based classifier is not sufficient to distinguish AF from other arrhythmias. Future work should concentrate on acquiring datasets with a high diversity of arrhythmias and employing new classification features.