Because a real-time monitoring using ECG signals is a challenging task, the pre-processing techniques used for ECG signal analysis are crucial for obtaining information that is further used for some more complex analysis, such as predictive analyses. We compared different settings of parameters needed for pre-processing of ECG signals in order to estimate the valuable information that can be further used for blood pressure classification. Two parameters were involved in the comparison: i) the signal length used for ECG segmentation; and ii) the cut-off frequency used for baseline removal. The first parameter is the parameter used for obtaining ECG segments that are further used, and the second one is the frequency used for baseline removal. Thirty different combinations, each a combination of a signal length and a cut-off frequency, were evaluated using a dataset that contains data from five commercially available ECG sensors. For signal lengths: 10 s, 20 s, and 30 s, were used for data segmentation, while the cut-off frequency for baseline removal starts from 0.05 Hz, till 0.50 Hz, with a step length of 0.05 Hz. The evaluation of these combinations was done in combination with complexity analysis used for features extraction that are further used for blood pressure classification. Experimental results, obtained using a data-driven approach by comparing the combinations using the results obtained from the classification for 17 performance measures, showed that a signal length of 30s carries the most information in a combination with cut-off frequency between 0.10Hz and 0.20Hz. Results contribute to the arguments published in the literature discussing the optimal ECG sample lengths needed for building predictive models, as well as the lower frequencies where the ECG components overlap with the baseline wander noise.