This paper presents a cardiac arrhythmia medical monitoring system that can modify and change the components of its processing chain to carry out the best treatment on electrocardiogram (ECG) signal. The most important feature to detect in the ECG is the QRS complex. However, the experience gathered over several years, shows that the proposed strategies to detect the QRS complex have reached an asymptotic detection performance. We propose to use a mixture of low-level and high-level information, called the current context, to pilot QRS detection algorithms in order to reduce the number of errors. The algorithms are piloted according to a set of piloting rules acquired by statistical analysis. Results of piloting three QRS detectors on five test ECGs corrupted by real clinical noise, show that the pilot enables to reduce the error rate from 14,3% to 10,6%. These results are useful to the development of a real-time monitoring system which can choose the best algorithm to recognize arrhythmias in clinical noisy context. The presented approach is not restricted to the QRS complex detection but can be extended to the processing of other biomedical signals.