Abstract
Respiration Rate (RR) is an important physiological
indicator and plays a major role in health deterioration monitoring.
Despite that, it has been neglected in hospital wards due to
inadequate nursing skills and insufficient equipment. ECG signal,
which is always monitored in a clinical setting, is modulated
by respiration which renders it a highly enticing mean for the
automatic RR estimation. In addition, accurate QRS detection is
pivotal to RR estimation from the ECG signal. The investigation
of QRS complexes is a continuing concern in ECG analysis
because current methods are still inaccurate and miss heart
beats. This paper presents a frequency domain RR estimation
method which uses a novel real-time QRS detector based on
Empirical Mode Decomposition (EMD). Another novelty of the
proposed work stems from the RR estimation in the frequency
domain as opposed to some of the current methods which rely
on a time domain analysis. As will be shown later, the RR
extraction in the frequency domain provides more accurate
results compared to the time domain methods. Moreover, our
novel QRS detector uses an adaptive threshold over a sliding
window and differentiates large Q- from R-peaks, facilitating a
more accurate RR estimation. The performance of our methods
was tested on real data from Capnobase dataset. An average
mean absolute error of less than 0.5 breath per minute was
achieved using our frequency domain method, compared to
6 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%.
indicator and plays a major role in health deterioration monitoring.
Despite that, it has been neglected in hospital wards due to
inadequate nursing skills and insufficient equipment. ECG signal,
which is always monitored in a clinical setting, is modulated
by respiration which renders it a highly enticing mean for the
automatic RR estimation. In addition, accurate QRS detection is
pivotal to RR estimation from the ECG signal. The investigation
of QRS complexes is a continuing concern in ECG analysis
because current methods are still inaccurate and miss heart
beats. This paper presents a frequency domain RR estimation
method which uses a novel real-time QRS detector based on
Empirical Mode Decomposition (EMD). Another novelty of the
proposed work stems from the RR estimation in the frequency
domain as opposed to some of the current methods which rely
on a time domain analysis. As will be shown later, the RR
extraction in the frequency domain provides more accurate
results compared to the time domain methods. Moreover, our
novel QRS detector uses an adaptive threshold over a sliding
window and differentiates large Q- from R-peaks, facilitating a
more accurate RR estimation. The performance of our methods
was tested on real data from Capnobase dataset. An average
mean absolute error of less than 0.5 breath per minute was
achieved using our frequency domain method, compared to
6 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 | 12th International Conference on Signal Processing and Communication Systems. ICSPCS 2018 |
| Editors | Tadeusz A Wysocki, Beata J Wysocki |
| Publisher | IEEE |
| ISBN (Electronic) | 978-1-5386-5602-0 |
| ISBN (Print) | 978-1-5386-5603-7 |
| DOIs | |
| Publication status | Published - 4 Feb 2019 |
| Event | 12th International Conference on Signal Processing and Communication Systems, ICSPCS 2018 - Cairns, Australia Duration: 17 Dec 2018 → 19 Dec 2018 |
Conference
| Conference | 12th International Conference on Signal Processing and Communication Systems, ICSPCS 2018 |
|---|---|
| Country/Territory | Australia |
| City | Cairns |
| Period | 17/12/18 → 19/12/18 |
Bibliographical note
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Keywords
- ECG-derived-respiration
- Empirical Mode Decomposition (EMD)
- Frequency domain analysis
- Local Signal Energy
- R-peak detection