Performance Comparison of SVM and ANN for Reversible ECG Data Hiding

  • Siddharth Bhalerao
  • Irshad Ahmad AnsariEmail author
  • Anil Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1154)


In the telemedicine industry, data security is a prime aspect because medical data is frequently transferred and stored over networks. In such scenario watermarking emerges as an important technique that ensures data security and integrity. In this work, use of SVM and ANN is investigated for a high capacity ECG data hiding technique. Watermarking is done using prediction error expansion (PEE). SVM and ANN are used to predict the ECG values. Two separate regression SVM models with linear kernel and Gaussian kernel are trained. ANN model used is a feed-forward deep neural network with three hidden layers. The proposed method provides a completely reversible watermarking. The patient’s confidential data is used as a watermark. For testing and training of algorithms, signals from MIT-BIH arrhythmia database are used. Performance of both SVM and ANN-based prediction models are evaluated using normalized cross-correlation, signal-to-noise ratio, and percentage residual difference. Linear SVM and deep ANN are comparable in performance and are better than Gaussian SVM.


Reversible medical data hiding Deep neural network Regression SVM 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Siddharth Bhalerao
    • 1
  • Irshad Ahmad Ansari
    • 1
    Email author
  • Anil Kumar
    • 1
  1. 1.Electronics and Communication EngineeringPDPM Indian Institute of Information Technology Design and ManufacturingJabalpurIndia

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