Aims: A major challenge in BCG measurements is their high sensitiveness to motion artifacts, which degrade signal quality. These motion artifacts depend on the position in which BCG is measured and are due to involuntary movements of the subjects, false contacts with the sensor, vibrations in the environment, etc. Several techniques have been developed, especially for BCG measurements during sleep, to automatically discard the corrupted segments and the amount of signal free of artifacts with respect to the whole recording is defined as the coverage factor. Nevertheless, current approaches to obtain it are mainly based on raw signal analysis, which can potentially discard signal segments of acceptable quality having significant fluctuations in amplitude due to factors such as respiratory rate or baseline drifts. To overcome this drawback, a novel technique which combines both the signal and its wavelet transform is proposed, intended to improve the coverage detection process.
Methods: An analysis based on standard deviation windows of BCG signal and its Continuous Wavelet Transform (CWT) is proposed which is compared against a more traditional technique based in raw signal analysis for 18 recordings obtained from the Kansas public database of BCGs measured in a lying position on a bed with a significant number of motion artifacts on them.
Results: The results in the table show the coverage factor using the two methods for the set of 18 recordings obtained from the database, for which an improvement in the coverage factor up to a 10 % has been obtained in some critical records such as "'X1028'".
Conclusion: The proposed technique has proven to be efficient for increasing the coverage factor in the recordings analyzed, which can be especially useful for continuous monitoring applications.