Improved Reconstruction for CS-Based ECG Acquisition in Internet of Medical Things

This paper presents an improved reconstruction approach for compressed sensing based ECG acquisition in Internet of Medical Things. The proposed method exploits the concepts of adaptive overcomplete dictionary and QRS detection in CS domain. Based on whether there is a QRS complex, the ECG frames to be reconstructed are divided into several categories and corresponding overcomplete dictionaries are trained to fit these different kinds of ECG frames. Specifically, QRS detection is first performed directly on the compressed measurements to determine the QRS morphology without actually reconstructing the signal in advance, and then a suitable overcomplete dictionary is chosen for the signal reconstruction. Because the selected dictionaries well fit the ECG frames, the reconstruction quality is consequently improved. Comparative experiments have been conducted, and the results well demonstrate the performance of the proposed approach.

Automated summary

This paper presents an improved reconstruction approach for compressed sensing based ECG acquisition in Internet of Medical Things by utilizing adaptive overcomplete dictionary and QRS detection. By dividing ECG frames into different categories based on the presence of a QRS complex and selecting suitable dictionaries for reconstruction, the proposed method improves reconstruction quality. Experimental results demonstrate the effectiveness of the approach.