Abstract—Electrocardiogram (ECG) signals are affected by various kinds of noise and artifacts that may impede correct recognition by automated monitoring or diagnosis systems. Independent component analysis (ICA) is considered as a new technique suitable for the separation and removal of diverse noises independent of ECG signals. This paper first proposes the application of independent component analysis to ECG signal pre-processing and then compares the performances of two major types of ICAs namely Infomax and Fast ICAs in ECG signal de-noising. The annotated benchmark samples from MIT-BIH arrhythmia database are used for experiments. We compare the signal to noise ratio improvements in the real ECG data with different ICA algorithms and the recognition rates. It is found that both types of ICA can effectively improve the ECG recognition in the presence of non-trivial artifacts, but FastICA slightly outperforms. However, it is worth mentioning that the Infomax algorithm might be further optimized.

Index Terms—electrocardiogram, independent component analysis, machine learning, pattern recognition

Cite: Mohammad Sarfraz, Francis F. Li, and Ateeq A. Khan, "Independent Component Analysis Methods to Improve Electrocardiogram Patterns Recognition in the Presence of Non-Trivial Artifacts," Journal of Medical and Bioengineering, Vol. 4, No. 3, pp. 221-226, June 2015. Doi: 10.12720/jomb.4.3.221-226