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A model to find optimal percentage of training and testing data for efficient ECG analysis using neural network

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Abstract

Electrocardiogram (ECG) data is one of the most important physiological parameter for detecting heartbeat, emotions and stress levels of patients. The problem is to develop a model that can diagnose an ECG data efficiently with higher accuracy overtime. In this paper, Authors have proposed a model that identifies the percentage division of data so as to get the maximum possible accuracy for a particular dataset. For experimental purpose, the authors have used neural networks for the analysis of the standard and raw data taken from MIT-BIH long-term ECG database using R as a platform. The database is divided into different ratios of training and testing data and the model is trained to attain the best percentage division of the particular patient’s data based upon its accuracy.

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Authors and Affiliations

  1. Manipal Institute of Technology, Manipal, Udupi, Karnataka, 576004, India

    Kartik Bhanot

  2. Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India

    Sateesh Kumar Peddoju

  3. Wireless and Cloud Computing Lab, Indian Institute of Technology Roorkee, Saharanpur Campus, Saharanpur, Uttar Pradesh, 247001, India

    Tushar Bhardwaj

Authors
  1. Kartik Bhanot
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  2. Sateesh Kumar Peddoju
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  3. Tushar Bhardwaj
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Corresponding author

Correspondence to Tushar Bhardwaj.

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Bhanot, K., Peddoju, S.K. & Bhardwaj, T. A model to find optimal percentage of training and testing data for efficient ECG analysis using neural network. Int J Syst Assur Eng Manag 9, 12–17 (2018). https://doi.org/10.1007/s13198-015-0398-7

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  • DOI: https://doi.org/10.1007/s13198-015-0398-7

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