title: Ensemble empirical mode decomposition based feature enhancement of cardio signals.
abstract: This paper presents an application of ensemble empirical mode decomposition method for enhancement of specific biological signal features. The application for two types of cardiological signals is presented in this article. Detection of fiducial points is a routine task for analyzing these signals. In a clinical situation, cardiological signals are usually corrupted by artifacts and finding exact time instances of various fiducial points is a challenge. Filtering approach for signal to noise ratio enhancing is traditionally and widely used in clinical practice. Methods, based on filtering, however, have serious limitations when it is necessary to find compromise between noise suppression and preservation of signal features. The proposed method uses ensemble empirical mode decomposition in order to suppress noise or enhance specific waves in the signal. Performance of the method was estimated by using clinical electrocardiogram and impedance cardiogram signals with synthetic baseline-wander, power-line and added Gaussian noise. In electrocardiogram application, an average estimation error of QRS complex length was 2.06-4.47%, the smallest in comparison to the reference methods. In impedance cardiogram application, the proposed method provided the highest cross-correlation coefficient between original and de-noised signal in comparison to reference methods. When the signal to noise ratio of the input signal was -12 dB, the method provided signal to error ratio of 33 dB in this case. The proposed method is adaptive to template and signal itself and thus could be applied to other non-stationary biological signals.
authors.affiliation: Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania. artjanu@ktu.lt
authors.forename: Artūras
authors.initials: A
authors.lastname: Janušauskas
authors.forename: Vaidotas
authors.initials: V
authors.lastname: Marozas
authors.forename: Arūnas
authors.initials: A
authors.lastname: Lukoševičius
mesh_terms: D000465:Algorithms; D002307:Cardiography, Impedance / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted