ECG-Derived Respiratory Rate in Atrial Fibrillation

et al. 2020

Sci Rep

Self Cite

Cardiorespiratory monitoring is crucial for the diagnosis and management of multiple conditions such as stress and sleep disorders. Therefore, the development of ambulatory systems providing continuous, comfortable, and inexpensive means for monitoring represents an important research topic. Several techniques have been proposed in the literature to derive respiratory information from the ECG signal. Ten methods to compute single-lead ECG-derived respiration (EDR) were compared under multiple conditions, including different recording systems, baseline wander, normal and abnormal breathing patterns, changes in breathing rate, noise, and artifacts. Respiratory rates, wave morphology, and cardiorespiratory information were derived from the ECG and compared to those extracted from a reference respiratory signal. Three datasets were considered for analysis, involving a total 59 482 onemin, single-lead ECG segments recorded from 156 subjects. The results indicate that the methods based on QRS slopes outperform the other methods. This result is particularly interesting since simplicity is crucial for the development of ECG-based ambulatory systems. Continuous monitoring of respiration plays a key role in the detection and management of different conditions, such as stress 1,2 and sleep disorders 3,4. Biomarkers like respiratory rate, breathing phases, and tidal volume are relevant for the detection of mental stress 1 , anxiety 2 , and sleep apnea events 5,6. In addition, the coupling between respiration and heart rate has been used as a biomarker for the aforementioned conditions 7,8 as well as for the understanding of the interactions between the cardiac and respiratory systems 9. Despite the importance of monitoring respiration, its recording requires the use of invasive and intrusive sensors like thermistors, spirometers, and respiratory belts. Even though these sensors are regularly used, for instance during polysomnographic recordings, their use in ambulatory systems is very limited since they not only interfere with natural breathing, but are often associated with high costs and low comfort. Different studies have shown that the respiratory rate, and even the respiratory wave morphology, can be approximated by ECG-derived respiration (EDR) 5,10-20. The derived signal is defined by certain morphological properties of the ECG particularly influenced by respiration. This influence can be explained by the respiratory-induced chest movements that cause changes in the position of the electrodes relative to the cardiac vector 21. Moreover, the filling and emptying of the lungs cause changes in the electrical impedance of the chest. As a result, the morphology of the ECG is modulated by respiration.

mentioning

confidence: 99%

A Comparative Study of ECG-derived Respiration in Ambulatory Monitoring using the Single-lead ECG

Varon

Morales

et al. 2020

Sci Rep

Self Cite

“…This choice was made because these techniques were shown to be the most robust techniques when tested in different scenarios [7], including highly non-stationary and noisy environments [6]. Furthermore, as these features are based exclusively on QRS morphology, they are applicable to all those situations where the QRS has a conventional morphology, including some arrhythmias such as atrial fibrillation [8].…”

Section: Discussionmentioning

confidence: 99%

An ECG-based System for Respiratory Rate Estimation Tested on a Wearable Armband During Daily Life

2020 Computing in Cardiology Conference (CinC)

Reljin

Bailón

et al. 2020

Self Cite

A pilot study on breathing rate (BR) estimation during daily life by using a wearable armband is presented. This wearable armband provides three electrocardiogram (ECG) channels, and BR was estimated from them by using ECG derived respiration (EDR) techniques based on respiration-related QRS morphology modulations: QRS slopes and R-wave angle. Five healthy volunteers wore the armband during 24 hours, with the only instruction not to exercise. In addition, reference ECG signals were simultaneously recorded by a market-available 3-channel Holter monitor. The percentage of armband's accurate BR estimations (differing less than 5% from the Holter estimation) with respect to the total number of Holter's estimations was computed (P 1). In addition, the percentage of accurate armband's BR estimations with respect to the total number of armband's estimations was also computed (P 2). P 1 ranged from 26.59% to 73.00% during non-bed time, and from 63.05% to 88.73% during bed time. P 2 ranged from 60.89% to 94.57% during non-bed time, and from 81.65% to 97.38% during bed time. These results are promising and suggest that the armband may be useful for BR monitoring in some applications. However, an artifact detector specifically focused on detecting those segments which are usable for BR detection needs to be developed.

“…An additional parameter was derived from these QRS slopes by computing their differences ( US -DS ). This parameter is called QRS slopes range (SR) and it was proposed in [25] for obtaining an EDR signal in the time domain that combines the information of both QRS slopes. This combination is faster than the combination proposed in [21] (see Section II-C), hence, SR may be more appropriate for wearables such as the armband from the point of view of computational time.…”

Section: B Electrocardiogram-derived Respiration Signalsmentioning

confidence: 99%

Electrocardiogram Derived Respiratory Rate Using a Wearable Armband

IEEE Trans. Biomed. Eng.

Reljin

Bailón

et al. 2021

Self Cite

A method for deriving respiratory rate from an armband, which records three-channel electrocardiogram (ECG) using three pairs of dry (no hydrogel) electrodes, is presented. The armband device is especially convenient for long-term (monthsyears) monitoring because it does not use obstructive leads nor hydrogels/adhesives, which cause skin irritation even after few days. An ECG-derived respiration (EDR) based on respirationrelated modulation of QRS slopes and R-wave angle approach was used. Moreover, we modified the EDR algorithm to lower the computational cost. Respiratory rates were estimated with the armband-ECG and the reference plethysmography-based respiration signals from 15 subjects who underwent breathing experiment consisting of five stages of controlled breathing (at 0.1, 0.2, 0.3, 0.4, and 0.5 Hz) and one stage of spontaneous breathing. The respiratory rates from the armband obtained a relative error with respect to the reference (respiratory rate estimated from the plethysmography-based respiration signal) that was not higher than 2.26% in median nor interquartile range (IQR) for all stages of fixed and spontaneous breathing, and not higher than 3.57% in median nor IQR in the case when the low computational cost algorithm was applied. These results demonstrate that respiration-related modulation of the ECG morphology are also present in the armband ECG device. Furthermore, these results suggest that respiration-related modulation can be exploited by the EDR method based on QRS slopes and R-wave angles to obtain respiratory rate, which may have a wide range of applications including monitoring patients with chronic respiratory diseases, epileptic seizures detection, stress assessment, and sleep studies, among others.