Key result
The proposed codec architecture with adaptive quantization and ANS-based entropy coding achieved 5-25% additional space savings over standard compressive sensing for ECG signals.
Why the study?
Compressive sensing is appealing for low-complexity ECG data compression in resource-limited wearable devices, but methods are needed to boost compression ratios without sacrificing reconstruction performance.
Effect estimate: 5-25% additional space savings
A novel compressive sensing codec architecture for ECG signals achieves 5-25% additional space savings over standard compressive sensing, potentially benefiting resource-limited wearable devices.
May improve data storage efficiency for resource-limited wearable ECG devices; leaves open whether these computational gains.
Compressive sensing (CS) is quite appealing as a low-complexity method for the compression of ECG data in resource-limited wearable devices. We propose a codec architecture comprising adaptive quantization and asymmetric numeral systems (ANS) based entropy coding of compressive measurements that can boost the compression ratio without sacrificing reconstruction performance. The quantized Gaussian entropy model for the compressive measurements is estimated directly from the data and is adapted dynamically to achieve better compression. We have tested our encoder with Block Sparse Bayesian learning as well as CS-NET sparse recovery algorithms on the MIT-BIH Arrhythmia database. Our encoder can achieve 5-25\% of additional space savings over compressive sensing. The software code implementing this codec and all scripts for the experimental studies conducted in this work have been released as opensource software on GitHub.
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Prasad et al. (2022) studied ECG data compression. Codec architecture with adaptive quantization and ANS-based entropy coding vs. Standard compressive sensing was evaluated on Space savings / compression ratio (5-25% additional space savings). The proposed codec architecture with adaptive quantization and ANS-based entropy coding achieved 5-25% additional space savings over standard compressive sensing for ECG signals.
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