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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sarfraz, M. Khan, A.A. Li, F.F. |
| Copyright Year | 2014 |
| Description | Author affiliation: Sch. of Comput., Sci. & Eng., Univ. of Salford, Salford, UK (Sarfraz, M.; Li, F.F.) || Electr. Eng., Salman bin Abdul Aziz Univ., Alkharj, Saudi Arabia (Khan, A.A.) |
| Abstract | 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. |
| Starting Page | 62 |
| Ending Page | 67 |
| File Size | 474016 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479956692 |
| DOI | 10.1109/BIBM.2014.6999249 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-02 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Electrodes Accuracy independent component analysis Heart beat Electrocardiogram Electrocardiography Feature extraction Back propagation neural network machine learning pattern recognition |
| Content Type | Text |
| Resource Type | Article |
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