Contact-Based Methods for Measuring Respiratory Rate

Massaroni, Carlo; Nicolò, Andrea; Presti, Daniela Lo; Sacchetti, Massimo; Silvestri, Sergio; Schena, Emiliano

doi:10.3390/s19040908

Cited by 324 publications

(268 citation statements)

References 250 publications

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“…Besides ECG and PPG based methods, several additional concepts of estimating respiratory parameters have emerged. Other approaches used to estimate respiratory signals include respiratory sounds, breathing air temperature, humidity and components [37], the oscillometric cuff pressure, Korotkoff sounds, as well as the seismocardiogram (SCG) [38]. For the latter, it has been shown that the morphology of the vibrational cardiac waveforms varies with the respiratory volume [39,40], and methods of extracting respiratory phases [41,42], effort [43] and breathing states such as normal, breathless, long, and labored [44] have been proposed.…”

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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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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“…The generally accepted clinical standard for heart rate monitoring using devices is the use of ECG monitoring [12] or pulse frequency from a photoplethysmogram [13]. Respiratory rate monitoring in patients requiring continuous monitoring is typically performed via thoracic bioimpedance from ECG leads or capnography [14]. Blood pressure monitoring in clinical spaces is typically performed via an automated oscillometric cuff [15], while oxygen saturation monitoring is performed using a two-wavelength pulse oximeter, either as a standalone device or integrated into a vital signs monitor [16].…”

Section: Rationalementioning

confidence: 99%

“…Pulse oximetry, while now present in most smartphones [18] continues to rely on the same principle of the change in the absorbance of visible and infrared light of hemoglobin at different oxygen saturations [16]. Recent incremental innovations to other vital signs monitoring include new algorithms to better detect heart rate and respiratory rate [14] or the combination of multiple measurement devices into a single, more portable unit [19].…”

Section: Rationalementioning

confidence: 99%

Novel wearable and contactless monitoring devices to identify deteriorating patients in the clinical setting: a systematic review protocol

et al. 2020

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Background: With technological advances, there has been increasing interest in developing contactless and/or non-invasive wearable technologies that continuously monitor vital signs in the clinical setting, and in particular in the deteriorating patient. These devices as of yet have not been well validated in the clinical setting in the clinical ranges observed in a critically unwell patient. We will perform a systematic review of all novel wearable and contactless devices in the clinical setting with focus on degree of novelty and the range of vital signs captured. Methods: Ovid MEDLINE including Epub Ahead of Print and In-Process & Other Non-Indexed Citations, Ovid Embase, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials (CENTRAL) Health Technology Assessment (HTA) database (Ovid), CINAHL with Full Text, searches of the grey literature, cited references of eligible studies through Web of Science, and reference lists of eligible studies will be searched. Outcomes of interest will include the quality of studies in relation to reporting guidelines, limitations of non-invasive technology, and application in different clinical populations. We will perform a qualitative assessment of the novelty of the device and discuss its validation in deteriorating patients. Discussion: While novel monitoring devices are often proposed as a solution to problems with infection, discomfort, and frequency of monitoring in the clinical setting it has not yet been established which devices have been validated in clinical settings in the pathological ranges of vital signs that reflect patient deterioration. It is equally unclear what additional value these devices might provide. This systematic review will synthesize published data regarding devices that have been tested and validated in patients AND in a clinical setting AND in reference ranges that reflect severe illness. Systematic review registration: PROSPERO CRD42019130091

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“…In general, the main inconveniences are related to the correct and specific use of each contact sensor, the stress, pain, and irritation caused, mainly on some vulnerable skins, like neonates and burns patients [1]. For a review of contact-based methods and comparisons see [2]. Contactless breathing monitoring is a recent research interest for clinical and day-to-day applications; a review, and comparison of contactless monitoring techniques can be seen in [3].…”

Section: Introductionmentioning

confidence: 99%

A Contactless Respiratory Rate Estimation Method Using a Hermite Magnification Technique and Convolutional Neural Networks

2020

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The monitoring of respiratory rate is a relevant factor in medical applications and day-to-day activities. Contact sensors have been used mostly as a direct solution and they have shown their effectiveness, but with some disadvantages for example in vulnerable skins such as burns patients. For this reason, contactless monitoring systems are gaining increasing attention for respiratory detection. In this paper, we present a new non-contact strategy to estimate respiratory rate based on Eulerian motion video magnification technique using Hermite transform and a system based on a Convolutional Neural Network (CNN). The system tracks chest movements of the subject using two strategies: using a manually selected ROI and without the selection of a ROI in the image frame. The system is based on the classifications of the frames as an inhalation or exhalation using CNN. Our proposal has been tested on 10 healthy subjects in different positions. To compare performance of methods to detect respiratory rate the mean average error and a Bland and Altman analysis is used to investigate the agreement of the methods. The mean average error for the automatic strategy is 3.28 ± 3.33% with and agreement with respect of the reference of ≈98%.

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Contact-Based Methods for Measuring Respiratory Rate

Cited by 324 publications

References 250 publications

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

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

Novel wearable and contactless monitoring devices to identify deteriorating patients in the clinical setting: a systematic review protocol

A Contactless Respiratory Rate Estimation Method Using a Hermite Magnification Technique and Convolutional Neural Networks

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