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Objective <p>Tele-monitoring is a useful platform for remote monitoring of cardiac patients, where compression plays a significant role in reducing the link burden and memory utilization of the source device. This paper describes a new approach for lossless ECG compression based on a deep-learning method via an adaptive autoregressive integrated moving average (ARIMA) model.</p> Methods <p>Raw ECG signals were denoised and preprocessed to generate beat-cells for further processing. The ARIMA model uses the individual cardiac cycles to generate model parameters, which are then compressed. In this research, the optimal model hyperparameters were predicted by a deep autoencoder followed by a multilayer perceptron neural network (MLPNN) regressor combination. The predictor was tuned offline via particle swarm optimization (PSO), which produced the reference data for MLPNN tuning.</p> Results <p>The technique uses 46 records of mitdb under PhysioNet, including 10 major abnormal beats: H, A, V, P, L, R, a, f, F and j. Because of the adaptive nature, compression quality is high with negligible loss. No deviations in the clinical features of the reconstructed beats are found. The mean CR and PRD% values were 41.51 and 0.209%, respectively, which are superior to those reported in published research on ECG compression.</p> Conclusion <p>The proposed adaptive ECG compression model can be useful for real-time telemonitoring applications, efficient storage and transmission of streamlined data of critical patients under continuous monitoring.</p>

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Deep Learning Based Approach for Lossless ECG Compression

  • 期刊论文

  • 发表日期：

    2026年2月27日

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  • Anumita Mitra,
  • Palash Kundu,
  • Rajarshi Gupta

摘要

Objective

Tele-monitoring is a useful platform for remote monitoring of cardiac patients, where compression plays a significant role in reducing the link burden and memory utilization of the source device. This paper describes a new approach for lossless ECG compression based on a deep-learning method via an adaptive autoregressive integrated moving average (ARIMA) model.

Methods

Raw ECG signals were denoised and preprocessed to generate beat-cells for further processing. The ARIMA model uses the individual cardiac cycles to generate model parameters, which are then compressed. In this research, the optimal model hyperparameters were predicted by a deep autoencoder followed by a multilayer perceptron neural network (MLPNN) regressor combination. The predictor was tuned offline via particle swarm optimization (PSO), which produced the reference data for MLPNN tuning.

Results

The technique uses 46 records of mitdb under PhysioNet, including 10 major abnormal beats: H, A, V, P, L, R, a, f, F and j. Because of the adaptive nature, compression quality is high with negligible loss. No deviations in the clinical features of the reconstructed beats are found. The mean CR and PRD% values were 41.51 and 0.209%, respectively, which are superior to those reported in published research on ECG compression.

Conclusion

The proposed adaptive ECG compression model can be useful for real-time telemonitoring applications, efficient storage and transmission of streamlined data of critical patients under continuous monitoring.