The subautoregression (SAR) model for ECG compression achieved very low bit rates (~70 b/s) with a reconstruction error of less than 10%, proving superior to conventional linear prediction.
Does the SAR/LTP model improve ECG signal compression compared to the conventional STP method?
A new ECG compression algorithm based on long-term prediction achieves high compression ratios with low reconstruction error, outperforming conventional short-term prediction methods.
A new algorithm for ECG signal compression is introduced. The compression system is based on the subautoregression (SAR) model, known also as the long-term prediction (LTP) model. The "periodicity" of the ECG signal is employed in order to further reduce redundancy, thus yielding high compression ratios. The suggested algorithm was evaluated using an in-house database. Very low bit rates on the order of 70 b/s are achieved with a relatively low reconstruction error (percent rms difference-PRD) of less than 10%. The algorithm was compared, using the same database, with the conventional linear prediction (short-term prediction--STP) method, and was found superior at any bit rate. The suggested algorithm can be considered a generalization of the recently published average beat subtraction method.
Nave et al. (Fri,) conducted a other in ECG signal compression. Subautoregression (SAR) / long-term prediction (LTP) model vs. Conventional linear prediction (short-term prediction--STP) method was evaluated on Compression ratio and reconstruction error (percent rms difference-PRD). The subautoregression (SAR) model for ECG compression achieved very low bit rates (~70 b/s) with a reconstruction error of less than 10%, proving superior to conventional linear prediction.
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