Longterm biosignal recordings, such as overnight sleep recordings (so-called polysomnographies, PSG), often vary in signal quality and signal shape. Movement artifacts occur frequently. This may impede successful application of automated processing algorithms designed for well-defined short-term recordings. To test existing algorithms on suitability for PSG analysis, and develop robust analysis tools, an environment that offers efficient application of biosignal methods on comprehensive and representative reference data is required. In this article, a Grid based biosignal processing platform is presented, that provides a large set of clinical PSG reference data collected within the SIESTA project. To date, different processing and evaluation methods with focus on polysomnographic electrocardiogram (PSG-ECG) based analysis are implemented. First results for heart rate analysis of PSG-ECG are given, including the introduction of a performance quality measure for non-annotated PSG-ECG. Different publicly available heart beat detection algorithms have been tested. As an example, the wqrs algorithm, provided by the PhysioNet shows a sensitivity of over 99% and a positive predictive value of over 94% on the PhysioNet's PSG reference data. Processed on the SIESTA data, it only detects around 60% of the heart beats, resulting in a low average performance quality of 0.28. Evaluation of further algorithms has led to the development of an improved, robust algorithm with a high average performance quality of 0.98.