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A Novel Feature Level Fusion for Heart Rate Variability Classification Using Correntropy and Cauchy-Schwarz Divergence

Journal of Medical Systems volume 42, Article number: 109 (2018) Cite this article

Abstract

Heart rate variability (HRV) analysis has become a widely used tool for monitoring pathological and psychological states in medical applications. In a typical classification problem, information fusion is a process whereby the effective combination of the data can achieve a more accurate system. The purpose of this article was to provide an accurate algorithm for classifying HRV signals in various psychological states. Therefore, a novel feature level fusion approach was proposed. First, using the theory of information, two similarity indicators of the signal were extracted, including correntropy and Cauchy-Schwarz divergence. Applying probabilistic neural network (PNN) and k-nearest neighbor (kNN), the performance of each index in the classification of meditators and non-meditators HRV signals was appraised. Then, three fusion rules, including division, product, and weighted sum rules were used to combine the information of both similarity measures. For the first time, we propose an algorithm to define the weights of each feature based on the statistical p-values. The performance of HRV classification using combined features was compared with the non-combined features. Totally, the accuracy of 100% was obtained for discriminating all states. The results showed the strong ability and proficiency of division and weighted sum rules in the improvement of the classifier accuracies.

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Funding

“This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors”.

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Affiliations

  1. Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran

    Ateke Goshvarpour & Atefeh Goshvarpour

  2. Khayyam Boulevard, Khayyam 20, Number 54, Mashhad, Khorasan Razavi, Iran

    Atefeh Goshvarpour

Authors
  1. Ateke Goshvarpour
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  2. Atefeh Goshvarpour
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Correspondence to Atefeh Goshvarpour.

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The authors declare that they have no conflict of interest.

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This article is part of the Topical Collection on Image & Signal Processing

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Goshvarpour, A., Goshvarpour, A. A Novel Feature Level Fusion for Heart Rate Variability Classification Using Correntropy and Cauchy-Schwarz Divergence. J Med Syst 42, 109 (2018). https://doi.org/10.1007/s10916-018-0961-2

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  • DOI: https://doi.org/10.1007/s10916-018-0961-2

Keywords

  • Feature level fusion
  • Correntropy
  • Cauchy-Schwarz divergence
  • Heart rate variability
  • Meditation