There are many commercially available sensors for acquiring electrocardiogram (ECG) signals, and the predictive analyses are extremely welcome for real-time monitoring in public healthcare. One crucial task of such analysis is the selection of the pre-processing parameters for the ECG signals that carry the most valuable information. For this reason, we extended our previous work by proposing a framework, known as ECGpp, which can be used for selecting the best pre-processing parameters for ECG signals (like signal length and cut-off frequency) that will be involved in predictive analysis. The novelty of the framework is in the evaluation methodology that is used, where an ensemble of performance measures are used to rank and select the most promising parameters. Thirty different combinations of a signal length and a cut-off frequency were evaluated using a data set that contains data from five commercially available ECG sensors in the case of blood pressure classification. Evaluation results show that a signal length of 30 s carries the most valuable information, while it was demonstrated that lower cut-off frequencies, where the ECG components overlap with the baseline wander noise, can also provide promising results. Additionally, the results were tested according to the selection of the performance measures, and it was shown that they are robust to inclusion and exclusion of correlated measures.