Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Charlton, Peter; Birrenkott, Drew A.; Bonnici, Timothy; Pimentel, Marco A. F.; Johnson, Alistair E. W.; Alastruey, Jordi; Tarassenko, Lionel; Watkinson, Peter; Beale, Richard; Clifton, David A.

doi:10.1109/rbme.2017.2763681

Cited by 247 publications

(201 citation statements)

References 217 publications

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“…RR can also be obtained by extracting the RSA component from other vital signs that can be acquired by wearable devices, such as ECG and PPG. The derivation of RR from cardiac signals mainly consists of extraction of respiratory signals via different modulations (baseline wander, amplitude, and frequency) from the signal and estimation of RR from the extracted respiratory signal [51]. With a fusion algorithm at the stage of respiratory extraction with estimations from multiple signals, it is possible to improve robustness against motion artefact hence increase estimation accuracy.…”

Section: B Respiratory Ratementioning

confidence: 99%

“…With a fusion algorithm at the stage of respiratory extraction with estimations from multiple signals, it is possible to improve robustness against motion artefact hence increase estimation accuracy. Further technical details are provided in the article by Charlton et al that reviews the RR estimation from ECG and PPG [51]. The advantage of indirect estimation of RR is that these techniques can be easily integrated into commercially existing wearable devices, adding the value of RR monitoring to the existing functionality.…”

Section: B Respiratory Ratementioning

confidence: 99%

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Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Ding

Clifton

et al. 2021

IEEE Rev. Biomed. Eng.

190

142

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Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death.A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

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Section: B Respiratory Ratementioning

confidence: 99%

Section: B Respiratory Ratementioning

confidence: 99%

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Ding

Clifton

et al. 2021

IEEE Rev. Biomed. Eng.

190

142

View full text Add to dashboard Cite

show abstract

“…While the development of an unobtrusive home sleep test system took important steps forward, the expert opinion remains that further research regarding the minimum number of parameters and methods of signal acquisitions is required [34]. A promising technique for unobtrusive respiratory signal estimation is ECG-derived respiration (EDR), which is commonly used in conjunction with other signal modalities such as photoplethysmography (PPG) [35]. An obvious advantage of EDR in the case of an available ECG is that no additional sensor is needed to estimate respiratory parameters.…”

Section: Clinical Background and State-of-the-artmentioning

confidence: 99%

Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVET and Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram

Klum

Tigges

Pielmuş

et al. 2020

Sensors

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Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HD TM and Osypka ICON-Core TM . In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.

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“…The rate of respiration in humans depends on age and cardiopulmonary status, but typically is 12-18 breaths per minute (0.2-0.3 Hz.) in adults (Charlton et al, 2018). This frequency range is above the Nyquist folding frequency of most single-band fMRI studies.…”

Section: Introductionmentioning

confidence: 84%

Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity

Gratton¹,

Coalson

Dworetsky

et al. 2019

Preprint

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AbstractDenoising fMRI data requires assessment of frame-to-frame head motion and removal of the biases motion introduces. This is usually done through analysis of the parameters calculated during retrospective head motion correction (i.e., ‘motion’ parameters). However, it is increasingly recognized that respiration introduces factitious head motion via perturbations of the main (B0) field. This effect appears as higher-frequency fluctuations in the motion parameters (> 0.1 Hz, here referred to as ‘HF-motion’), primarily in the phase-encoding direction. This periodicity can sometimes be obscured in standard single-band fMRI (TR 2.0 – 2.5 s.) due to aliasing. Here we examined (1) how prevalent HF-motion effects are in seven single-band datasets with TR from 2.0 - 2.5 s and (2) how HF-motion affects functional connectivity. We demonstrate that HF-motion is relatively trait-like and more common in older adults, those with higher body mass index, and those with lower cardiorespiratory fitness. We propose a low-pass filtering approach to remove the contamination of high frequency effects from motion summary measures, such as framewise displacement (FD). We demonstrate that in most datasets this filtering approach saves a substantial amount of data from FD-based frame censoring, while at the same time reducing motion biases in functional connectivity measures. These findings suggest that filtering motion parameters is an effective way to improve the fidelity of head motion estimates, even in single band datasets. Particularly large data savings may accrue in datasets acquired in older and less fit participants.Highlights-Single-band fMRI motion traces show factitious high-frequency content (HF-motion)-The magnitude of HF-motion relates to age and other demographic factors-HF-motion elevates framewise displacement (FD) and causes data loss-Substantial fMRI data can be recovered from censoring by filtering motion traces-Filtering motion traces reduces motion artifacts in functional connectivity

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Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Cited by 247 publications

References 217 publications

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVET and Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram

Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity

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