Name
Abstract | ||
|---|---|---|
QRS detection based on ECG signal is the most straightforward method for heart beat detection. However, existing QRS detection methods do not work well when ECG signal is contaminated or missing. Other physiological signals also contain information about cardiac activity and ECG. Their information can be explored for robust heart beat detection. As part of the PhysioNet/Computing in Cardiology Challenge 2014, this study proposed a multimodal information fusion framework for robust heart beat detection. The framework consisted of three steps: 1) QRS detection. 2) Remove spurious QRS detection using pulsatile signal if it is available. 3) Refine the remaining beat detection and interpolate missed beats. Results show that the algorithm can sufficiently reduce spurious QRS detection and accurately fill in missed beats. |
Year | Venue | Keywords |
|---|---|---|
2014 | CinC | bioelectric potentials,cardiovascular system,electrocardiography,medical signal detection,medical signal processing,qrs detection based ecg signal,cardiac activity,heart beat detection,multimodal information fusion framework,physiological signals,pulsatile signal,electroencephalography,physiology,glass |
Field | DocType | Volume |
Cardiac activity,Heart beat,Computer science,Speech recognition,QRS complex,Beat detection,Electrocardiography,Information fusion,Missed beats,Spurious relationship | Conference | 41 |
ISSN | Citations | PageRank |
2325-8861 | 2 | 0.43 |
References | Authors | |
1 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Quan Ding | 1 | 59 | 7.72 |
| Yong Bai | 2 | 5 | 0.90 |
| Yusuf Bugra Erol | 3 | 4 | 1.84 |
| rebeca salasboni | 4 | 2 | 0.43 |
| Xiaorong Zhang | 5 | 54 | 9.15 |
| Li, Lei | 6 | 799 | 69.54 |
| Xiao Hu | 7 | 72 | 13.64 |