Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes

By Hossein Mamaghanian, Nadia Khaled, David Atienza Alonso, Pierre Vandergheynst. IEEE Transactions on Biomedical Engineering, 2011

724 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/884b246dbcfe6a83b455fe6f1a062d887b796d78

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Tzyy-Ping Jung
2. Bhaskar D. Rao
3. Zhilin Zhang
4. Scott Makeig
5. Bernard Ghanem
6. Jian Zhang

In Computational Mechanics

1. Zhilin Zhang
2. Bhaskar D. Rao
3. Scott Makeig
4. Tzyy-Ping Jung
5. David Paul Wipf
6. Philip Schniter
7. Jeremy P. Vila
8. Yonina C. Eldar

In Radiology, Nuclear Medicine and Imaging

1. Tzyy-Ping Jung
2. Bhaskar D. Rao
3. Zhilin Zhang

Citations in the corpus

• Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning (2012, methodology): “[10] showed that when using a sparse binary matrix as the sensing matrix, CS can greatly extend sensor lifetime and reduce energy consumption while achieving competitive compression ratio (CR), compared to a wavelet-based compression method.”
• Smart e-Health Gateway: Bringing intelligence to Internet-of-Things based ubiquitous healthcare systems (2015, background): “3) Data Filtering: Physiological systems of the human body such as cardiovascular, nervous and muscular systems generate bio-signals that are the primary source of information for assessing the patient health status.”