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Quality controlled 2D ECG compression using adaptive fourier decomposition

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Abstract

This work presents the recently developed Adaptive Fourier Decomposition (AFD) approach-based ECG compression technique that ensures considerable data reduction at guaranteed reconstructed signal quality. Unlike existing decomposition techniques with fixed Basis functions, AFD selects distinct Basis function in accordance with the input signal resulting in fast energy convergence with good signal fidelity. However, the complex nature of AFD coefficients and Basis functions restrain the compression efficiency. In the proposed technique AFD algorithm is modified to obtain integer valued Basis functions causing considerable improvements in the Compression Ratio (CR) and reconstructed signal quality. Additionally, the complex-valued AFD coefficients are encoded with the lossless Adaptive Bit Length Encoding (ABLE) approach which further modify the compression efficiency. The comprehensive experimentation of the proposed technique was done on various standard and self-recorded databases on varying values of decomposition levels (N) and window size (W). The proficiency of the technique was further analysed by computing optimum N in accordance with the pre-defined distortion to achieve maximum CR. Average Percentage Root Mean Square difference (PRD%), Weighted Wavelet PRD (WWPRD), Wavelet Energy based Diagnostic Distortion (WEDD), Peak Signal to noise Ratio (PSNR), CR and Quality Score (QS) obtained are 0.68, 18.24, 6.86, 47.19, 22.11 and 37.24 respectively when measured on 48 records of MIT-BIH arrhythmia database of 5 min duration at N = 50 and W = 2000. The experimental results demonstrated the competency of the proposed technique with excellent CR at negligible reconstruction error as compared to the other state of the art techniques.

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Data Availability

Name of datasets and references have been provided under Section 5. The datasets analyzed for the study are publicly available. https://physionet.org/about/database/.

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

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Authors and Affiliations

  1. Electronics and Communication Engineering Department, Dr B R Ambedkar National Institute of Technology, Jalandhar, 144011, India

    Neetika Soni & Indu Saini

  2. Electronics and Communication Engineering Department, Guru Nanak Dev University, Regional Campus, Jalandhar, 144007, India

    Neetika Soni & Butta Singh

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  1. Neetika Soni
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Correspondence to Butta Singh.

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Soni, N., Saini, I. & Singh, B. Quality controlled 2D ECG compression using adaptive fourier decomposition. Multimed Tools Appl 83, 43607–43634 (2024). https://doi.org/10.1007/s11042-023-17318-1

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