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Electrocardiogram beat classification via coupled boosting by filtering and preloaded mixture of experts

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Abstract

The role of electrocardiogram (ECG) as a noninvasive technique for detecting and diagnosing cardiac problems cannot be overemphasized. This paper introduces a modified version of boosted mixture of experts for the classification of three types of ECG beats. Our two-step preloading procedure, along noise injection, also regarded as smoothing regularization, proved to be a promising, effective, and safe means of classifying arrhythmias. The proposed model, according to the nature of implementation, is called coupled boosting by filtering and preloaded mixture of experts. The experimental results show our proposed method have better classification rate against other compared methods. Comparative evaluation is accomplished with ECG signals from MIT–BIH arrhythmia database.

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Acknowledgments

The authors wish to thank Shahid Rajaee Teacher Training University for funding this project.

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Authors and Affiliations

  1. Brain and Intelligent Systems Research Laboratory, Department of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University, P.O. Box: 16785-163, Tehran, Iran

    Reza Ebrahimpour, Naser Sadeghnejad & Nima Mohammadi

  2. South Tehran Branch, Islamic Azad University, No. 209, North Iranshahr St., Tehran, Iran

    Atena Sajedin

Authors
  1. Reza Ebrahimpour
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  2. Naser Sadeghnejad
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  3. Atena Sajedin
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  4. Nima Mohammadi
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Correspondence to Reza Ebrahimpour.

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Ebrahimpour, R., Sadeghnejad, N., Sajedin, A. et al. Electrocardiogram beat classification via coupled boosting by filtering and preloaded mixture of experts. Neural Comput & Applic 23, 1169–1178 (2013). https://doi.org/10.1007/s00521-012-1063-6

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  • DOI: https://doi.org/10.1007/s00521-012-1063-6

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