A major challenge in BCG measurements is their high sensitivity to motion artifacts, which degrade the signal quality. Several techniques have been developed, especially for BCG measurements during sleep, to automatically discard corrupted segments. To evaluate them, the coverage factor is defined as the amount of artifact-free signal with respect to the entire recording. However, current approaches to obtain it are mainly based on the analysis of the raw signal, which may discard signal segments of acceptable quality that exhibit significant amplitude fluctuations due to factors such as respiratory rate or deviations from baseline. To overcome this drawback, a novel technique combining both the signal and its wavelet transform is proposed, which is compared to the more traditional technique based on the raw signal variance. Results obtained from the analysis of 18 recordings from the BCG Kansas public database show a 10% coverage factor increase in critical records, which may be particularly valuable for continuous monitoring applications.