Abstract
Breathing Rate (BR) plays a key role in health deterioration
monitoring. Despite that, it has been neglected due to inadequate nursing
skills and insufficient equipment. ECG signal, which is always monitored
in a hospital ward, is affected by respiration which makes it a highly
appealing way for the BR estimation. In addition, the latter requires accurate
R-peak detection, which is a continuing concern because current
methods are still inaccurate and miss heart beats. This study proposes
a frequency domain BR estimation method which uses a novel real-time
R-peak detector based on Empirical Mode Decomposition (EMD) and
a blind source ICA for separating the respiratory signal. The originality
of the BR estimation method is that it takes place in the frequency domain
as opposed to some of the current methods which rely on a time
domain analysis, making the estimation more accurate. Moreover, our
novel QRS detector uses an adaptive threshold over a sliding window
and differentiates large Q-peaks from R-peaks, facilitating a more accurate
BR estimation. The performance of our methods was tested on real
data from Capnobase dataset. An average mean absolute error of less
than 0.7 breath per minute was achieved using our frequency domain
method, compared to 15 breaths per minute of the time domain analysis.
Moreover, our modified QRS detector shows comparable results to
other published methods, achieving a detection rate over 99.80%.
monitoring. Despite that, it has been neglected due to inadequate nursing
skills and insufficient equipment. ECG signal, which is always monitored
in a hospital ward, is affected by respiration which makes it a highly
appealing way for the BR estimation. In addition, the latter requires accurate
R-peak detection, which is a continuing concern because current
methods are still inaccurate and miss heart beats. This study proposes
a frequency domain BR estimation method which uses a novel real-time
R-peak detector based on Empirical Mode Decomposition (EMD) and
a blind source ICA for separating the respiratory signal. The originality
of the BR estimation method is that it takes place in the frequency domain
as opposed to some of the current methods which rely on a time
domain analysis, making the estimation more accurate. Moreover, our
novel QRS detector uses an adaptive threshold over a sliding window
and differentiates large Q-peaks from R-peaks, facilitating a more accurate
BR estimation. The performance of our methods was tested on real
data from Capnobase dataset. An average mean absolute error of less
than 0.7 breath per minute was achieved using our frequency domain
method, compared to 15 breaths per minute of the time domain analysis.
Moreover, our modified QRS detector shows comparable results to
other published methods, achieving a detection rate over 99.80%.
| Original language | English |
|---|---|
| Title of host publication | International Conference on Time Series and Forecasting |
| Subtitle of host publication | ITISE 2018: Theory and Applications of Time Series Analysis |
| Publisher | Springer |
| Pages | 363-377 |
| ISBN (Electronic) | 978-3-030-26036-1 |
| ISBN (Print) | 978-3-030-26035-4 |
| DOIs | |
| Publication status | Published - 19 Oct 2019 |
| Event | International Conference on Time Series and Forecasting, ITISE 2018 - Granada, Spain Duration: 19 Sep 2018 → 21 Sep 2018 |
Conference
| Conference | International Conference on Time Series and Forecasting, ITISE 2018 |
|---|---|
| Country/Territory | Spain |
| City | Granada |
| Period | 19/09/18 → 21/09/18 |