AI Article Synopsis
- A new method for ECG signal de-noising was developed, utilizing Independent Component Analysis (ICA) that combines JADE source separation with a binary decision tree for noise identification and removal.
- The effectiveness of this method was tested against wavelet-based de-noising using data from Physionet, with the root mean square error (RMSE) used as the evaluation criterion.
- The proposed algorithm showed comparable results for standard noise types but significantly outperformed the wavelet method in handling uncommon noises, particularly from electrode cable movements.
Article 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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4048160 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0098450 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!