Abstract
Classification accuracy is vital in practical application of automatic ECG diagnostics. This paper aims at enhancing accuracy of ECG signals classification. We propose a statistical method to segment heartbeats from ECG signal as precisely as possible, and use the combination of independent component analysis (ICA) features and temporal feature to describe multi-lead ECG signals. To obtain the most discriminant features of different class, we introduce the minimal-redundancy-maximal-relevance feature selection method. Finally, we designed a vote strategy to make the decision from different classifiers. We test our method on the MIT-BIT Arrhythmia Database, achieving a high accuracy.
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Wu, Y., Zhang, L. (2011). ECG Classification Using ICA Features and Support Vector Machines. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_18
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DOI: https://doi.org/10.1007/978-3-642-24955-6_18
Publisher Name: Springer, Berlin, Heidelberg
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