Using independent component analysis to obtain feature space for reliable ECG Arrhythmia classification

Sarfraz, M, Khan, AA and Li, FF ORCID: 2014, 'Using independent component analysis to obtain feature space for reliable ECG Arrhythmia classification' , in: 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) , IEEE, pp. 62-67.

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Electrocardiogram (ECG) reflects the activities of the human heart and reveals hidden information on its structure and behaviour. The information is extracted to gain insights that assist explanation and identification of diverse pathological conditions. This was traditionally done by an expert through visual inspection of ECGs. The complexity and tediousness of this onus hinder long-term monitoring and rapid diagnosis, computerised and automated ECG signal processing are therefore sought after. In this paper an algorithm that uses independent component analysis (ICA) to improve the performance of ECG pattern recognition is proposed. The algorithm deploys the basis functions obtained via the ICA of typical ECG to extract ICA features of ECG signals for further pattern recognition, with the hypothesis that components of an ECG signal generated by different parts of the heart during normal and arrhythmic cardiac cycles might be independent. The features obtained via the ICA together with the R-R interval and QRS segment power are jointly used as the input to a machine learning classifier, an artificial neural network in this case. Results from training and validation of the MIT-BIH Arrhythmia database shows significantly improved performance in terms of recognition accuracy. This new method also allows for the reduction of the number of inputs to the classifier, simplifying the system and increasing the real-time performance. The paper presents the algorithm, discusses the principle algorithm and presents the validation results.

Item Type: Book Section
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Publisher: IEEE
ISBN: 9781479956692
Related URLs:
Funders: University of Salford
Depositing User: Dr Francis F. Li
Date Deposited: 12 May 2016 08:09
Last Modified: 15 Feb 2022 20:47

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