Correlation of Respiratory Signals and Electrocardiogram Signals via Empirical Mode Decomposition
Type
ThesisAuthors
El Fiky, Ahmed OsamaAdvisors
Kosel, Jürgen
Committee members
Alouini, Mohamed-Slim
Sundaramoorthi, Ganesh

Program
Electrical EngineeringDate
2011-05-24Permanent link to this record
http://hdl.handle.net/10754/136671
Metadata
Show full item recordAbstract
Recently Electrocardiogram (ECG) signals are being broadly used as an essential diagnosing tool in different clinical applications as they carry a reliable representation not only for cardiac activities, but also for other associated biological processes, like respiration. However, the process of recording and collecting them has usually suffered from the presence of some undesired noises, which in turn affects the reliability of such representations.Therefore, de-noising ECG signals became a hot research field for signal processing experts to ensure better and clear representation of the different cardiac activities. Given the nonlinear and non-stationary properties of ECGs, it is not a simple task to cancel the undesired noise terms without affecting the biological physics of them. In this study, we are interested in correlating the ECG signals with respiratory parameters, specifically the lung volume and lung pressure. We have focused on the concept of de-noising ECG signals by means of signal decomposition using an algorithm called the Empirical Mode Decomposition (EMD) where the original ECG signals are being decomposed into a set of intrinsic mode functions (IMF). Then, we have provided criteria based on which some of these IMFs have been adapted to reconstruct de-noised ECG version. Finally, we have utilized de-noised ECGs as well as IMFs for to study the correlation with lung volume and lung pressure. These correlation studies have showed some clear resemblance especially between the oscillations of ECGs and lung pressures.Citation
El Fiky, A. O. (2011). Correlation of Respiratory Signals and Electrocardiogram Signals via Empirical Mode Decomposition. KAUST Research Repository. https://doi.org/10.25781/KAUST-1WKJ8ae974a485f413a2113503eed53cd6c53
10.25781/KAUST-1WKJ8