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<p>The electrocardiogram (ECG) is a widely acknowledged clinical tool for diagnosis of cardiovascular diseases (CVDs). In low and middle income countries (LMICs), the lack of connected health (CH) systems often results in ECG data being shared via paper records, risking privacy breaches. One potential solution is embedding ECG data within a quick response (QR) code, enabling secure transmission of clinically essential information while preserving patient privacy. In this paper, we propose a learning-based compression method that preserves essential clinical information which is further encoded losslessly using the Brotli algorithm for QR code embedding. We have experimentally validated our approach on publicly available dataset containing ECG recordings from healthy individuals and with 26 distinct CVD pathologies. Key performance is assessed using Percentage Root-Mean-Square Difference (PRD), Structural Similarity Index (SSIM), and Compression Factor (CF) along with morphological features comparisons between original and decompressed lead-II ECG signals. At CF of 82.37, we have attained PRD of 2.70%, SSIM of 0.94 for lead-II ECG signal, and PRD of 2.80%, SSIM of 0.94 for lead-I ECG signal. Our method outperforms state-of-the-art approaches, enabling secure, efficient, and scalable ECG integration into CH systems for continuous monitoring and early detection of CVDs.</p>

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Learning to compress electrocardiogram signals on a quick response code

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月1日

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  • Apoorva Srivastava,
  • Dipayan Dewan,
  • Amit Patra,
  • Debdoot Sheet

摘要

The electrocardiogram (ECG) is a widely acknowledged clinical tool for diagnosis of cardiovascular diseases (CVDs). In low and middle income countries (LMICs), the lack of connected health (CH) systems often results in ECG data being shared via paper records, risking privacy breaches. One potential solution is embedding ECG data within a quick response (QR) code, enabling secure transmission of clinically essential information while preserving patient privacy. In this paper, we propose a learning-based compression method that preserves essential clinical information which is further encoded losslessly using the Brotli algorithm for QR code embedding. We have experimentally validated our approach on publicly available dataset containing ECG recordings from healthy individuals and with 26 distinct CVD pathologies. Key performance is assessed using Percentage Root-Mean-Square Difference (PRD), Structural Similarity Index (SSIM), and Compression Factor (CF) along with morphological features comparisons between original and decompressed lead-II ECG signals. At CF of 82.37, we have attained PRD of 2.70%, SSIM of 0.94 for lead-II ECG signal, and PRD of 2.80%, SSIM of 0.94 for lead-I ECG signal. Our method outperforms state-of-the-art approaches, enabling secure, efficient, and scalable ECG integration into CH systems for continuous monitoring and early detection of CVDs.