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Hypertension detection and indexing from cardiac ECM image analysis

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Abstract

Hypertension is considered as a global public health problem by the World Health Organization. The risk of cardiovascular mortality grows with hypertension. The disease can be diagnosed by combined analysis of cardiac extracellular matrix (ECM) images and machine learning. This study proposes a novel method for automated classification of hypertension and its risk indexing using cardiac ECM images through machine learning. The method includes image pre-processing, Bi-dimensional Empirical Mode Decomposition (BEMD), feature extraction, feature selection, and classification guided by a statistical T-test for hypertension indexing. The proposed method applied over 300 cardiac ECM images and among them 150 belong to the normal group and the rest are from the hypertension group. The classification accuracy of the method is 98.9% with a sensitivity of 0.97 and specificity of 1. The F1 score, False Negative Rate, and False Positive Rate of the proposed method is 0.99, 0.02, and 0 respectively. Inspired by the classification accuracy, a unique Hypertension Risk Indexing (HRI) system has been developed focusing on minimum complexity of execution through prominent feature selection. Such an indexing mechanism can assist clinicians in the preliminary study of hypertension risk assessment.

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Acknowledgments

The authors would also like to thank Dr. M.K. Bhowmik, Assistant Professor, Department of Computer Science and Engineering, Tripura University, Suryamaninagar 799022, Tripura, India for his support during knowledge development in the area of image feature extraction and classification.

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  1. Department of Computer Science & Engineering, Techno College of Engineering Agartala, Agartala, Tripura, 799004, India

    Shawli Bardhan

  2. Department of Mechanical Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, 440010, India

    Sukanta Roga

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Bardhan, S., Roga, S. Hypertension detection and indexing from cardiac ECM image analysis. Multimed Tools Appl 83, 30541–30561 (2024). https://doi.org/10.1007/s11042-023-16746-3

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