Atrial fibrillation (AF) is the most common cardiac arrhythmia, but is currently under-diagnosed since it can be asymptomatic. Early detection of AF could be highly beneficial for the prevention of stroke, which is one major risk associated with AF, with a five fold increase. mHealth applications have been recently proposed for early screening of paroxysmal AF. Several automatic AF detections have been suggested, and they are mostly based on features extracted from the RR interval time-series, since this is more robust to ambulatory noise than p-wave based algorithms. The RR interval features highlight the irregularity and unpredictability of the rhythm due to the chaotic electrical conduction through the AV node. Such approach has proved to be accurate on openly available databases. However, current techniques are limited by their assumption of almost perfect R peak detection, and RR time-series features are usually estimated from manual annotations. Analysis of the huge amount of data an mHealth application may create has to be automated, robust to noise, and should incorporate a confidence index based on an estimation of the signal quality. In this study, we present an in depth analysis of the performance of AF detection algorithms as a function of noise and QRS detection performance. We show a linear decrease of AF detection accuracy with respect to the SNR. Finally, we will demonstrate how the use of an automatic signal quality index can ensure a given level of performance in AF detection, more than 95% AF detection accuracy by analyzing segments with a median SQI over 0.8.