Name
Title | ||
|---|---|---|
Separating respiratory influences from the tachogram: Methods and their sensitivity to the type of respiratory signal |
Abstract | ||
|---|---|---|
Respiration is one of the main modulators causing heart rate variability (HRV). However, when interpreting studies of HRV, the effect of respiration is largely ignored. We, therefore, previously proposed to take respiratory influences into account by separating the tachogram in a component that is related to respiration and one that contains all residual variations. In this study, we aim to investigate the sensitivity of two of such separation methods, i.e. one based on an ARMAX model and another one based on orthogonal subspace projection (OSP), towards different respiratory signal types, such as nasal airflow (the reference), thoracic and abdominal efforts, and three ECG-derived respiratory (EDR) signals. The sensitivity of both separation methods to the type of respiratory signal is evaluated by assessing the information transfer from the reference respiratory signal to the residual tachogram, where the latter is obtained using each time a different type of respiratory signal. The results show that OSP is the least sensitive to the different types of respiratory signals. Even when an EDR signal obtained using kernel principal component analysis is used, OSP yields a correct separation in 13 out of 18 recordings, demonstrating that in many cases, the separation of the tachogram can successfully be conducted even if only the ECG is available. |
Year | Venue | Keywords |
|---|---|---|
2014 | CinC | electrocardiography,medical signal processing,pneumodynamics,principal component analysis,armax model,ecg derived respiratory signals,abdominal efforts,heart rate variability,kernel principal component analysis,nasal airflow,orthogonal subspace projection,respiration,respiratory signal type,sensitivity,separating respiratory influence,tachogram,thoracic efforts,entropy,stress |
Field | DocType | Volume |
Residual,Respiration,Pattern recognition,Subspace topology,Heart rate variability,Kernel principal component analysis,Speech recognition,Artificial intelligence,Respiratory system,Mathematics,Principal component analysis | Conference | 41 |
ISSN | Citations | PageRank |
2325-8861 | 1 | 0.36 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Devy Widjaja | 1 | 33 | 4.99 |
| Carolina Varon | 2 | 92 | 22.90 |
| dries testelmans | 3 | 1 | 0.36 |
| bertien buyse | 4 | 1 | 0.36 |
| Luca Faes | 5 | 175 | 28.91 |
| S. Van Huffel | 6 | 260 | 32.75 |