Results for the power line interference artefact.

<p>Horizontal axis shows level of added noise. Boxplots shows RMSE for used database of signals. Filtered methods that were used are differentiated by colors. Those boxplots where notches are marked with bold line around borders means that they means are not significantly different. We can observe that our algorithm works similarly to wavelet based de-noising and significantly better than filtering algorithm.</p

Examples of artefacts artificially added to ECG signals.

<p>From top to the bottom: 50 Hz power line interference, EMG, base line wander and electrode cable movement.</p

Independent Component Analysis and Decision Trees for ECG Holter Recording De-Noising

<div><p>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.</p></div

Features used for training decision tree using CART algorithm.

<p>Features left in decision tree after pruning are marked bold.</p

Results for the electrode cable movement artefact.

<p>Horizontal axis shows level of added noise. Boxplots shows RMSE for used database of signals. Filtered methods that were used are differentiated by colors. We can observe that ICA based method outperforms both referential methods and its results are significantly better, as it can be seen in figure.</p

Frequency response of post-processing low pass FIR filter with first zero at 117 Hz, delay 5 samples and gain 0.93.

<p>Frequency response of post-processing low pass FIR filter with first zero at 117 Hz, delay 5 samples and gain 0.93.</p

Decision tree training and pruning.

<p>First noise is added to the ECG data then components are estimated using JADE algorithm and components were manually labelled into noisy and noise-free groups. Features are computed and using them and annotations binary decision tree is trained using CART algorithm. Then the tree is passed to cross-validation and pruning and final pruned tree is created.</p

Evaluation process.

<p>Evaluation process.</p

Results for the base line wander artefact.

<p>Horizontal axis shows level of added noise. Boxplots shows RMSE for used database of signals. Filtered methods that were used are differentiated by colors. We can observe that our algorithm achieves good results. The wavelet filtering algorithm has difficulties with this type of the artefact due to its simulation as slow sinus wave.</p