Key result
An algorithm combining Independent Component Analysis and decision trees achieved significantly better de-noising results for electrode cable movement artefacts compared to standard and wavelet-based filtering.
An ICA and decision tree-based de-noising method effectively reduces ECG noise, particularly outperforming standard methods in removing electrode cable movement artifacts.
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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Kužílek et al. (2014) studied ECG noise (n=382). Independent Component Analysis (JADE) and Decision Trees vs. Standard filtering and wavelet-based de-noising was evaluated on Root mean square error (RMSE) between original ECG and filtered data. An algorithm combining Independent Component Analysis and decision trees achieved significantly better de-noising results for electrode cable movement artefacts compared to standard and wavelet-based filtering.
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