Compressed Sensing System Considerations for ECG and EMG Wireless Biosensors

By Anna M. R. Dixon, Emily G. Allstot, Daibashish Gangopadhyay, David J. Allstot. IEEE Transactions on Biomedical Circuits and Systems, 2012

317 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a3fb5770cdb4058e4a49c2fd823f579a2ffd75ac

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 Signal Processing

1. Zhilin Zhang
2. Tzyy-Ping Jung
3. Scott Makeig
4. Bhaskar D. Rao
5. Arnaud Delorme
6. Julie Anne Onton
7. Philip Schniter
8. Jeremy P. Vila

Citations in the corpus

• A Survey on Wireless Body Area Networks: Technologies and Design Challenges (2014, methodology): “The following is a nonexhaustive list of applications that can benefit from WBAN usage: electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), pulse oximetry, drugs delivery, post operative and temperature monitoring, glucose level, toxins, blood pressure, etc..”
• Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning (2012, methodology): “Although CS has achieved some successes in adult ECG telemonitoring [10], [12], [13], it encounters difficulties in FECG telemonitoring.”