Name
Abstract | ||
|---|---|---|
Gastrointestinal extracellular slow wave recordings are providing critical information about normal motility and pathophysiology of the gut. Processing the signals is an important adjunct to acquire clinically and physiologically meaningful analysis. In stomach and intestine in vivo slow wave recordings, ventilation (or respiratory) artifacts can be prominent, which hinders identification and analysis of slow wave profiles. Here, we introduce an algorithm to suppress these artifacts without distorting the slow wave morphology. The algorithm generates a cycle averaged ventilation signal by shifting the raw signal by its ventilation period. The cycle averaged ventilation signal is then subtracted from the slow wave recording. The algorithm was validated using signal to noise ratio (SNR) and Pearson correlation coefficient (PCC) on synthetic signals and SNR with experimental recordings. With synthetic data, SNR and PCC, improved by 7.29 +/- 1.14 dB and 0.23 +/- 0.15. With experimental data SNR improved by 2.66 +/- 0.60 dB. This method improves the slow wave signal content revealing a more accurate estimate of the slow wave morphology. The application of this method will lead to improved analysis of slow wave recordings to understand gut function. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/EMBC.2017.8037431 | 2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Computer vision,Ventilation (architecture),Computer science,Signal-to-noise ratio,Artificial intelligence,Acoustics,Electrical engineering | Conference | 2017 |
ISSN | Citations | PageRank |
1094-687X | 0 | 0.34 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Niranchan Paskaranandavadivel | 1 | 6 | 9.30 |
| Saeed Alighaleh | 2 | 0 | 1.35 |
| Peng Du | 3 | 10 | 11.12 |
| G. O'Grady | 4 | 1 | 1.48 |
| Leo K Cheng | 5 | 70 | 37.48 |