Role of independent component analysis in intelligent ECG signal processing

Sarfraz, M

Abstract

The Electrocardiogram (ECG) reflects the activities and the attributes of the human heart and
reveals very important hidden information in its structure. The information is extracted by
means of ECG signal analysis to gain insights that are very crucial in explaining and
identifying various pathological conditions. The feature extraction process can be
accomplished directly by an expert through, visual inspection of ECGs printed on paper or
displayed on a screen. However, the complexity and the time taken for the ECG signals to be
visually inspected and manually analysed means that it‟s a very tedious task thus yielding
limited descriptions. In addition, a manual ECG analysis is always prone to errors: human
oversights.
Moreover ECG signal processing has become a prevalent and effective tool for research and
clinical practices. A typical computer based ECG analysis system includes a signal preprocessing, beats detection and feature extraction stages, followed by classification.
Automatic identification of arrhythmias from the ECG is one important biomedical
application of pattern recognition. This thesis focuses on ECG signal processing using
Independent Component Analysis (ICA), which has received increasing attention as a signal
conditioning and feature extraction technique for biomedical application.
Long term ECG monitoring is often required to reliably identify the arrhythmia. Motion
induced artefacts are particularly common in ambulatory and Holter recordings, which are
difficult to remove with conventional filters due to their similarity to the shape of ectopic
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beats. Feature selection has always been an important step towards more accurate, reliable and
speedy pattern recognition. Better feature spaces are also sought after in ECG pattern
recognition applications.
Two new algorithms are proposed, developed and validated in this thesis, one for removing
non-trivial noises in ECGs using the ICA and the other deploys the ICA extracted features to
improve recognition of arrhythmias. Firstly, independent component analysis has been studied
and found effective in this PhD project to separate out motion induced artefacts in ECGs, the
independent component corresponding to noise is then removed from the ECG according to
kurtosis and correlation measurement.
The second algorithm has been developed for ECG feature extraction, in which the
independent component analysis has been used to obtain a set of features, or basis functions
of the ECG signals generated hypothetically by different parts of the heart during the normal
and arrhythmic cardiac cycle. ECGs are then classified based on the basis functions along
with other time domain features. The selection of the appropriate feature set for classifier has
been found important for better performance and quicker response. Artificial neural networks
based pattern recognition engines are used to perform final classification to measure the
performance of ICA extracted features and effectiveness of the ICA based artefacts reduction
algorithm.
The motion artefacts are effectively removed from the ECG signal which is shown by beat
detection on noisy and cleaned ECG signals after ICA processing. Using the ICA extracted
feature sets classification of ECG arrhythmia into eight classes with fewer independent
components and very high classification accuracy is achieved.

Citation

Sarfraz, M. Role of independent component analysis in intelligent ECG signal processing. (Thesis). University of Salford

• Thesis Type: Thesis
• Deposit Date: Mar 26, 2015
• Publicly Available Date: Mar 26, 2015
• Award Date: Dec 4, 2014