A deep learning-based adaptive ARIMA model for lossless ECG compression achieved a mean compression ratio of 41.51 and PRD of 0.209%, outperforming previously reported methods.
Does a deep learning-based adaptive ARIMA model improve lossless ECG compression quality?
A novel deep learning-based adaptive ARIMA model achieves high-quality lossless ECG compression, potentially improving real-time telemonitoring applications.
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.
Mitra et al. (Fri,) conducted a other in ECG compression (n=46). Deep learning-based adaptive ARIMA model was evaluated on Compression ratio (CR) and percentage root-mean-square difference (PRD%). A deep learning-based adaptive ARIMA model for lossless ECG compression achieved a mean compression ratio of 41.51 and PRD of 0.209%, outperforming previously reported methods.