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Kernel-based nonlinear dimensionality reduction for electrocardiogram recognition

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Abstract

The human heart is a complex system that reveals many clues about its condition in its electrocardiogram (ECG) signal, and ECG supervising is the most important and efficient way of preventing heart attacks. ECG analysis and recognition are both important and tempting topics in modern medical research. The purpose of this paper is to develop an algorithm which investigates kernel method, locally linear embedding (LLE), principal component analysis (PCA), and support vector machine(SVM) algorithms for dimensionality reduction, features extraction, and classification for recognizing and classifying the given ECG signals. In order to do so, a nonlinear dimensionality reduction kernel method based LLE is proposed to reduce the high dimensions of the variational ECG signals, and the principal characteristics of the signals are extracted from the original database by means of the PCA, each signal representing a single and complete heart beat. SVM method is applied to classify the ECG data into several categories of heart diseases. Experimental results obtained demonstrated that the performance of the proposed method was similar and sometimes better when compared to other ECG recognition techniques, thus indicating a viable and accurate technique.

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Acknowledgments

This work is supported by Foundation of National Natural science No. 10671030.

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Authors and Affiliations

  1. School of Applied Mathematics, University of Electronic Science and Technology of China, 610054, Chengdu, People’s Republic of China

    Xuehua Li & Lan Shu

  2. College of Computer Science, Sichuan University, 610065, Chengdu, People’s Republic of China

    Hongli Hu

Authors
  1. Xuehua Li
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  2. Lan Shu
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  3. Hongli Hu
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Correspondence to Xuehua Li.

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Li, X., Shu, L. & Hu, H. Kernel-based nonlinear dimensionality reduction for electrocardiogram recognition. Neural Comput & Applic 18, 1013–1020 (2009). https://doi.org/10.1007/s00521-008-0231-1

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  • DOI: https://doi.org/10.1007/s00521-008-0231-1

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