Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
