Sub-Nyquist Sampling of ECG Signals With Differentiated VPW Optimization Model

In recent years, developments in wearable technologies and wireless communication have enabled wearable ECG monitoring. Long-term ECG monitoring result in large amounts of data. To sub-Nyquist sampling ECG signal, recently developed finite rate of innovation (FRI) technology is a practical option. However, existing FRI sampling schemes generally suffered from large model mismatch errors and insufficient reconstruction accuracy. In this paper, a sub-Nyquist sampling scheme based on the differentiated VPW optimization model is proposed for ECG signals sampling. The original ECG signal is modeled as a linear combination of several differentiated Variable Pulse Width (VPW) pulses and a model mismatch error signal. Then, a two-channel sampling framework is proposed to sample ECG signals with a sub-Nyquist rate. To improve the accuracy of the reconstruction, optimizing the reconstruction is considered. We formulate the objective function of the optimization to minimize the model mismatch error signal. A particle filter-based optimization method is proposed to solve the objective function with a high-dimensional variable. Block coordinate descent (BCD) technique is considered in the proposed optimization method. Finally, we conduct several simulations with real ECG signals from MIT-BIH Arrhythmia Database to test the performance of our proposed scheme. Simulation results have demonstrated the reconstruction accuracy of our proposed scheme outperforms other related schemes.