title: Independent component analysis and decision trees for ECG holter recording de-noising.
abstract: We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA). This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standard filtering a wavelet- based de-noising method was used. Freely data available at Physionet medical data storage were evaluated. Evaluation criteria was root mean square error (RMSE) between original ECG and filtered data contaminated with artificial noise. Proposed algorithm achieved comparable result in terms of standard noises (power line interference, base line wander, EMG), but noticeably significantly better results were achieved when uncommon noise (electrode cable movement artefact) were compared.
authors.affiliation: Department of Cybernetics, FEE, CTU in Prague, Prague, Czech Republic.
authors.forename: Jakub
authors.initials: J
authors.lastname: Kuzilek
authors.affiliation: Department of Cybernetics, FEE, CTU in Prague, Prague, Czech Republic; Czech Institute of Informatics, Robotics, and Cybernetics, CTU in Prague, Prague, Czech Republic.
authors.forename: Vaclav
authors.initials: V
authors.lastname: Kremen
authors.affiliation: Department of Cardiovascular Diseases, ICRC, St. Anne's Hospital in Brno, Brno, Czech Republic.
authors.forename: Filip
authors.initials: F
authors.lastname: Soucek
authors.affiliation: Department of Cybernetics, FEE, CTU in Prague, Prague, Czech Republic.
authors.forename: Lenka
authors.initials: L
authors.lastname: Lhotska
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D003663:Decision Trees; D015716:Electrocardiography, Ambulatory / Q000379:methods*; D025341:Principal Component Analysis; D059629:Signal-To-Noise Ratio