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Independent component analysis and decision trees for ECG holter recording de-noising

J. Kuzilek, V. Kremen, F. Soucek, L. Lhotska,

. 2014 ; 9 (6) : e98450. [pub] 20140606

Language English Country United States

Document type Journal Article, Research Support, Non-U.S. Gov't

Persistent link   https://www.medvik.cz/link/bmc15032003

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.

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