The R peak detection of an ECG signal is the basis of virtually any further processing and any error caused by this detection will propagate to further processing stages. Despite this, R peak detection algorithms and annotated databases often allow large error tolerances around 10%, masking any error introduced. In this paper we have revisited popular ECG R peak detection algorithms by applying sample precision error margins. For this purpose we have created a new open access ECG database with sample precision labelling of both standard Einthoven I, II, III leads and from a chest strap. 25 subjects were recorded and filmed while sitting, solving a math test, operating a handbike, walking and jogging. Our results show that using an error margin with sample precision, common R peak detection algorithms perform much worse than previously reported. In addition, there are significant performance differences between detectors which can have detrimental effects on applications such as heartrate variability, thus leading to meaningless results.

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