Key result
The proposed under-sampling method for ECG signals significantly reduced reconstruction error compared to existing methods, achieving a PRD as low as 2.29% and an SRR of 11.77 dB.
Why the study?
Existing under-sampling methods for ECG wearable devices suffer from model mismatch, which negatively impacts signal reconstruction accuracy.
A novel ECG under-sampling method based on the Gaussian second-order derivative model improves signal reconstruction accuracy and robustness, potentially reducing power consumption in IoT wearable devices.
This article presents a novel under-sampling method for ECG signals, aimed at reducing the sampling rate and power consumption in IoT-based ECG wearable devices. The key contribution addresses the common issue of model mismatch in existing methods, which negatively impacts signal reconstruction accuracy. Initially, the ECG signal is modeled as a linear combination of several Gaussian second-order derivative functions, which can be efficiently represented with only a few parameters, thus mitigating the problem of large model matching errors. To further enhance reconstruction accuracy, an improved two-channel finite rate of innovation sampling framework is introduced, effectively addressing the nonideal effects caused by the low-pass filter during sampling. Additionally, a modified annihilating filter reconstruction algorithm is proposed, allowing high-precision signal reconstruction using a small number of sampling points to estimate parameters. The validity of the proposed method is confirmed through simulations with real ECG signals from the MIT-BIH arrhythmia database, and a hardware platform is developed to verify its feasibility in a practical system. Experimental results demonstrate that, compared to the existing methods, the proposed approach significantly reduces reconstruction error (achieving a PRD as low as 2.29% and an SRR of 11.77 dB), and exhibits better robustness in noisy environments.
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Huang et al. (2024) studied ECG signal processing. Under-sampling method based on the Gaussian second-order derivative model vs. Existing methods was evaluated on Reconstruction error (PRD and SRR). The proposed under-sampling method for ECG signals significantly reduced reconstruction error compared to existing methods, achieving a PRD as low as 2.29% and an SRR of 11.77 dB.
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