Clinical validation of a contactless respiration rate monitor

Scientific Reports volume 13, Article number: 3480 (2023) Cite this article

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Respiratory rate (RR) is an often underestimated and underreported vital sign with tremendous clinical value. As a predictor of cardiopulmonary arrest, chronic obstructive pulmonary disease (COPD) exacerbation or indicator of health state for example in COVID-19 patients, respiratory rate could be especially valuable in remote long-term patient monitoring, which is challenging to implement. Contactless devices for home use aim to overcome these challenges. In this study, the contactless Sleepiz One+ respiration monitor for home use during sleep was validated against the thoracic effort belt. The agreement of instantaneous breathing rate and breathing rate statistics between the Sleepiz One+ device and the thoracic effort belt was initially evaluated during a 20-min sleep window under controlled conditions (no body movement) on a cohort of 19 participants and secondly in a more natural setting (uncontrolled for body movement) during a whole night on a cohort of 139 participants. Excellent agreement was shown for instantaneous breathing rate to be within 3 breaths per minute (Brpm) compared to thoracic effort band with an accuracy of 100% and mean absolute error (MAE) of 0.39 Brpm for the setting controlled for movement, and an accuracy of 99.5% with a MAE of 0.48 Brpm for the whole night measurement, respectively. Excellent agreement was also achieved for the respiratory rate statistics over the whole night with absolute errors of 0.43, 0.39 and 0.67 Brpm for the 10th, 50th and 90th percentiles, respectively. Based on these results we conclude that the Sleepiz One+ can estimate instantaneous respiratory rate and its summary statistics at high accuracy in a clinical setting. Further studies are required to evaluate the performance in the home environment, however, it is expected that the performance is at similar level, as the measurement conditions for the Sleepiz One+ device are better at home than in a clinical setting.

Chronic diseases represent a remarkable health burden. Non-communicable diseases accounted for 74% of deaths globally in 2019, with ischemic heart disease (IHD), stroke, and chronic obstructive pulmonary disease (COPD) being the top 3 leading causes1.

Due to their morbidity and mortality, these conditions induce an increasing economic and social burden in a growing and aging population with the demand for innovative approaches to disease management.

Changes in respiratory rate (RR) is a known indicator of serious conditions in lung diseases like cystic fibrosis (CF) or COPD. Recent studies reported that a high RR was the most important predictor of cardiopulmonary arrest in hospitals2.

In 2017, COPD was estimated to affect ~ 300 million people worldwide, with a relative increase of 5.9% between 1990 and 20173. It is a preventable disease, characterized by persistent respiratory symptoms. A substantial part of COPD management is the prevention of exacerbations, defined as an acute worsening of symptoms resulting in additional therapy4. Within the past decade, it has been—and is still—of great interest to find strategies to diagnose COPD exacerbations earlier and/or prevent them completely, as they impact disease progression negatively. Studies suggest that long-term RR monitoring at home could be a valid tool to predict exacerbations5,6,7, but more investigation is needed to confirm these results and to find an easy-to-use solution for most patients.

With at least 26 million people affected in 2017, heart failure (HF) is another main cause of morbidity in elderly patients8. Recently, ambient sensor technology was used in a pilot approach to detect early signs of HF decompensation—among others, an increase in RR, heart rate (HR) and body movement in bed could be detected weeks before decompensation9. Even in the prediction of adverse events in IHD itself, RR monitoring is proved to be an integral part of the assessment of cardiac arrest prediction10.

Most recently, the COVID-19 pandemic highlighted the importance of RR monitoring, revealing the need for alternative medical solutions, including remote patient monitoring11.

Despite the clinical relevance and importance, long-term monitoring of RR faces some difficulties. For instance, just the fact that RR is measured can affect the patients breathing and change results. Moreover, RR is sensitive to various stressors, such as cognitive load, emotional stress, heat, cold, and physical effort12.

Non-contact biomotion sensing offers multiple advantages. Sensors can be deployed in a home setting, preferably during nighttime, which can help to eradicate the difficulties mentioned above. Due to their non-intrusive design and no action needed from patients’ side (given the device is placed correctly once), this technology can provide instantaneous RR for several hours in a natural setting. This might be the basis for trend analyses and novel monitoring approaches, combined with other parameters, to constantly advance disease management.

Various contactless approaches have been adopted. Mainly, the technologies can be grouped in four different categories; radiofrequency (RF) based systems and pressure sensor-based systems are already far in development, while audio and visual-based systems are in a more experimental stage. The latter mainly rely on advanced algorithms to extract relevant parameters from either audio signals13 or different visual signals14,15,16,17,18. Most pressure sensor-based systems use piezoelectric or electromechanical film pressure sensors and consist of a pad that is either placed on or below the mattress of the user during sleep19,20,21,22,23.

Finally, various devices and research projects use radiofrequency-based technology.

Doppler radar technology has attracted attention in the medical field as it can detect small amplitude vibration motions caused by breathing excursion and cardiac movement thus can be used for vital sign detection and sleep monitoring24. Recent advancements in radar systems have demonstrated their ability to capture physiological signals such as respiration frequency, heart rate and body movement in a contactless fashion25,26,27,28. Electromagnetic waves travel to the surface of the chest cavity and reflect from it. The antenna receives the reflected waveform containing information on the chest displacement. When the body is at rest, this movement is mainly caused by respiration and heart contractions. With an advanced digital signal processing pipeline, from respiration and heart contraction motions, respiration and heart rate can be estimated in nearly real-time, with very high accuracy. However, to date none of these systems is classified as a medical-grade diagnostic tool, mostly due to the lack of a proper clinical validation.

In this study, we aim to validate a novel contactless breathing monitoring solution based on the Doppler radar technology and advanced signal processing, developed to measure precise movements of the thorax caused by breathing effort and extract RR and breathing patterns for sleep monitoring and diagnosis (Sleepiz One+, Sleepiz AG, Switzerland). The performance of the device in detecting RR was benchmarked in a clinical setting against the thoracic effort belt.

This paper covers studies validating RR with a contactless sleep monitor (CSM) against the thoracic effort belt (TREB) of the polysomnography (PSG) setup, in two different clinics (Klinik Lengg, Switzerland and Ruhrlandklinik, Germany).

In a monocentric, prospective, open-label non-randomized validation study at Klinik Lengg, the CSM was validated against PSG over 20 min on resting participants and during sleep (Study 1).

Additionally, we analyzed a dataset obtained as part of a larger study at Ruhrlandklinik, where the participants underwent a single overnight recording in the sleep laboratory with a PSG setup and the Sleepiz device (Study 2).

In Study 1, patients suspected to suffer from sleep-related disorders or chronic cardiac, respiratory, or neuromuscular disorders were recruited from the neurorehabilitation ward of Klinik Lengg. The subjects included in Study 2 were patients of the Ruhrlandklinik, with a suspected or diagnosed sleep-related breathing disorder.

In both studies, the patients were ≥ 18 years old, and could consent to electrophysiological routine assessment (PSG) in writing. Exclusion criteria were previous enrolment into the current study, being a study investigator or one of their relatives, employees, and other dependent persons, having an implanted electrical device, pregnancy and inability to follow the study procedures, e.g. due to language problems or psychological disorders.

The Sleepiz One+ is a contactless respiration monitor whose operating principle is continuous-wave Doppler radar. The device is placed on the bedside table or a stand beside the user's bed and points at the user's thorax from a 40–50 cm distance. From this position, it transmits a continuous electromagnetic signal at a fixed frequency of 24 GHz (output power < 18 dBm) which is reflected at the user's thorax, while being minimally influenced by clothing or blankets. With a beam aperture of 80° in horizontal- and 34° in vertical plane, the Sleepiz One+'s field of view from the specified position covers the user's whole thorax-abdomen region. The reflected signal is received and pre-processed using a quadrature receiver29, resulting in two output channels BI(t) and BQ(t) which contain information on the movement in the whole thorax-abdomen region as distance changes of the user relative to the measurement device24,30 (Fig. 1).

Schematic of radar operation. T(t) transmitted electromagnetic signal (f = 24 GHz), R(t) reflected signal, BI(t) in-phase channel, received signal multiplied by transmitted signal, low-pass filtered, BQ(t) quadrature channel, received signal multiplied by 90° offset of transmitted channel, low-pass filtered. Figure provided by Sleepiz AG, Zürich, Switzerland; appeared in unpublished poster related to47.

The Sleepiz One+ data is transmitted to a cloud server, where it is analyzed using proprietary software which identifies and extracts rhythmic thorax and abdomen movements related to breathing motions to provide RR estimates, whose validation is the topic of this work.

The Sleepiz One+ is able to operate with two people in the same bed, but records only the respiration rate of the person closest to the device. Furthermore, multiple devices can operate in the same room, as long as their field of views do not intersect. Within this study, all participants were recorded with no other people in the bed, and with one device per room.

Each participant was monitored for one night simultaneously with the Sleepiz One+ and a full PSG setup (SOMNOtouch RESP, Somnomedics) including electrocardiogram, electroencephalogram, airflow (nasal cannula), respiratory effort (thorax and abdomen belts) and pulse oximetry. The Sleepiz One+ device was placed beside the bed at a distance of 50 cm pointing at the participant's thorax (Fig. 2). The person included in the image has provided informed consent to publish the image. All enrolled participants underwent the same procedure.

Placement of Sleepiz One+ device.

Each participant underwent an overnight recording in the sleep laboratory with a full PSG setup (Nox A1, Nox Medical), including a TREB), which was the channel used for the performance assessment. The Sleepiz One+ was placed beside the bed at a distance of 50 cm pointing at the participant's thorax.

Data from each device, namely the TREBs of the PSG systems and the Sleepiz device, were exported and converted to a Python dictionary containing its raw data, measurement start time, and sampling rate. Next, devices were time-synchronized with each other. Finally, quality assessment was performed. The signals were visually assessed to identify artifacts, i.e. regions that are not interpretable, due to the patient moving or the sensors being positioned suboptimally, leading to a signal-to-noise ratio insufficient for analysis. These were removed from further analysis. The criteria used to exclude participants for analysis are specified below.

For each patient, the 20-min analysis window to be included for instantaneous RR performance assessment was selected by identifying a window of 20 min of the recording, where the person was calmly lying in bed, within the first 3 h after the recording setup had been completed. The window was selected based on the following three conditions: (1) the reference device had started recording, (2) the Sleepiz device had started recording, and (3) less than 10 min of total duration where either the patient was moving, as detected by the Sleepiz device, or there were artifacts in RR estimates from the TREB.

The purpose of these conditions was to ensure the correctness of the reference instantaneous RRs and that a sufficient portion of the 20 min could be used for analysis (i.e., it was movement, breath irregularities and artifact-free), also considering that these conditions were most likely to be met in the first 3 h of sleep.

If no analysis window fulfilling all three conditions was identified in the first three hours of the recording, as determined by the Sleepiz device start time, the patient was excluded from the analysis. No sleep stage information was used for the selection of the analysis windows.

Participants were excluded from the RR statistics analysis if either the TREB or the Sleepiz data presented artifacts for 60% or more of the time-points between analysis start and end times. The start time would be the time of device which started to record later of the two and end time would the time of the device ended earlier of the two.

Exclusion criteria were the same as for Study 1 for RR statistics analysis.

We define instantaneous RR as the number of breaths in a time window of 60 s (epoch), where consecutive time windows have an overlap of 55 s, i.e., they present a 5 s offset.

Instantaneous RR was computed for the data from the TREB through the following procedure:

The signals were smoothed with a 7th order Butterworth low-pass filter with a cut-off frequency of 0.6 Hz. The filter cut-off was chosen based on the assumption of a maximum RR of 35Brpm.

The inhalation times were identified by performing a quantile normalization and finding maxima in the resulting signal fulfilling properties related to the peak prominence and minimum distance between peaks. The identified inhalation times were visually reviewed to ensure their correctness.

The instantaneous RR was derived by taking the inverse of the median time between inhalations.

The computed instantaneous RR from TREB inhalation times was not post-processed through any other means. Exhale times were not used for the RR computation due to challenges in precisely identifying the time of complete exhale; the exhalation curve is frequently flatter than the inhalation curve, and contains multiple peaks (Fig. 3, top row).

Data processing pipeline. Top row: estimation of instantaneous respiratory rate from thoracic chest effort band (TREB). The raw TREB data, presented in arbitrary units (a.u.) is first smoothed, inhalation times identified, and, from these, instantaneous respiratory rate is computed (see "Computation of instantaneous respiratory rate" for a detailed description). Middle row: estimation of instantaneous respiratory rate from Sleepiz One+ data. The raw signals (BI(t) and BQ(t)), presented in a.u. are used to compute the participant's chest displacement, from which statistics are extracted and used to compute respiratory rate. Bottom row: the instantaneous respiratory rate for the whole night is used to compute summary statistics, i.e. the 10%, 50%, and 90% percentiles of the respiratory rate distribution.

RR was computed for the Sleepiz One+ data following a similar procedure. The patient's chest movement was first computed from the raw data from the Sleepiz One+, consisting of channels BI(t) and BQ(t), using an arctangent demodulation algorithm. The chest movement was then filtered and normalized, and the inhalation and exhalation times were identified from the resulting signal. This information was used to compute a probability density over RRs in the range of 5–35 Brpm. In other words, for each RR within the range of 5 to 35 Brpm, the probability of the patient having that particular RR, given the observed inhalation and exhalation times, was computed. The probability density was used to obtain a RR per epoch, as well as to estimate the prediction confidence, i.e., the confidence of the algorithm in the RR prediction made being correct. If the confidence in the prediction is below a threshold, no RR is provided. It is worth noting that times where the patient is tossing and turning are automatically detected by the software and removed from the analysis, as these can lead to errors in the computation of RR.

RR statistics consisted of the minimum, average, and maximum RR over the recording. To remove the influence of outliers on these metrics, we computed them using the data percentiles, i.e. through the 10%, 50%, and 90% percentiles of instantaneous RR.

Instantaneous RR performance was assessed through the mean absolute error (MAE), and accuracy: the fraction of time-points where the estimates provided by the reference device and the Sleepiz device were within 3 Brpm.

The agreement between reference and Sleepiz device for RR statistics was evaluated by the absolute error (AE).

The minimum number of participants required for statistically significant outcomes of instantaneous RR accuracy was calculated using Eq. (1):

where Za determines the confidence level of the estimate (significance level), S is the sample standard deviation, and d is the minimal effect of interest. The significance level was set to 95%, corresponding to a value of Za to 1.64. The sample standard deviation was set to 5.2%, based on results from a previous pilot study. Finally, the minimal effect of interest was set to 2%. From these, we obtain a sample size of 19 participants. We included more than said number of participants in each center.

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Both included studies were conducted in accordance with the Declaration of Helsinki, the guidelines of Good Clinical Practice (GCP) issued by ICH, as well as the European Regulation on medical devices 2017/745, ISO 14155 and ISO 14971, and the Swiss Law and Swiss regulatory authority's requirements (Study 1) or the German Law and German regulatory authority's requirements (Study 2), respectively. Both studies were approved by the local ethics committees (Study 1: BASEC-Nr. 2020-00455, Cantonal Ethics Committee Zurich; Study 2: 19-8961-BO, Ethics committee of the medical faculty of the University of Duisburg-Essen) and written informed consent was taken from all individual participants.

Subjects suspected of suffering from sleep apnea were enrolled in the study based on their availability at the study clinics.

Sleep apnea has a high prevalence in subjects with diagnosed COPD, Asthma, cardiovascular diseases, Diabetes, Parkinson etc. Therefore, these subgroups were represented in the study population.

As per the pre-defined inclusion and exclusion criteria, 24 patients were included in study 1 and, among those, a suitable 20-min time segment meeting the quality criteria outlined above was identified for 19 participants (Table 1). In study 2, 187 subjects were enrolled, and 139 met the quality criteria and were considered for analysis (Table 2).

The standard for RR monitoring in clinical practice is visual counting of breaths. However, this method has been reported to have a high error31. Moreover, counting respiration cycles is not feasible for overnight recordings. Thus, we opted for an automated analysis of the TREB signals. It measures the expansion and contraction of the participant's chest, the same cue used for assessing respiration visually.

First, we measured instantaneous RR under controlled conditions; participants in study 1 were recorded during a full night in bed with a PSG setup, and a 20-min time segment where they were lying with little or no tossing and turning was selected for the analysis. Figure 4 shows the instantaneous RR estimated during the night for a participant, together with the selected 20-min time window.

Respiratory rate estimates of a participant from Study 1. Top: instantaneous respiratory rate estimates over the 20-min window used to assess performance. Bottom: respiratory rate over the night, with the 20-min window indicated via blue shading. Estimates from the Sleepiz One+ are presented in blue, and the ones from the thoracic chest effort band in black.

The data collected in 20-min time segment for 19 participants were assessed. The agreement of instantaneous RR between the TREB and Sleepiz One+ is shown in Fig. 5 and Table 3. The estimates were within 3 Brpm in all timepoints, thus the resulting accuracy of 100%. The MAE had an average value over participants of 0.39 Brpm, and the 95% limits of agreement (LoA) were [− 0.52, 1.16] Brpm (Fig. 5).

Bland–Altman plot of instantaneous respiratory rate performance. Top: results from Study 1. Bottom: results from Study 2. The solid blue line indicates the bias, while dashed red lines correspond to the 95% limits of agreement.

The agreement between the instantaneous RR estimates from the TREB and Sleepiz One+ were assessed for the whole night in study 2, where participants spent a night in a sleep lab with full PSG. The results are like those obtained in study 1, with accuracy being 99.5%, average MAE 0.48 Brpm, and the LoA [− 0.88 1.] Brpm (Table 3). Subgroup analysis of instantaneous breathing rate based on the diagnosed conditions in study 2 is shown in Table 4.

RR statistics have been shown to be predictive of health status5,6,7. We benchmarked the agreement of RR statistics between TREB and Sleepiz One+, quantified as the 10%, 50%, and 90% percentiles of instantaneous RR over the whole recording (Fig. 6), in study 2 for a total of 139 participants. The results, including a subgroup analysis based on the diagnosed conditions of the subjects, are presented in Table 4.

Scatter plot of respiratory rate statistics. Left: Study 1. Right: Study 2.

This report evaluated the accuracy of the Sleepiz One+ in assessing RR; in two separate studies on patients with disturbed breathing.

In study 1, instantaneous RR was assessed in a 20-min time window where participants were lying in bed without significant movement. This setting allowed the assessment of the Sleepiz One+'s performance in absence of abnormal respiration patterns which could confound results. In these conditions, the Sleepiz One+ estimated RR within 3 Brpm for all timepoints and participants included, demonstrating its efficacy in controlled conditions.

The performance of instantaneous RR throughout the night was assessed in Study 2, where patients with suspected or diagnosed sleep-related breathing disorders were monitored throughout the night. In this study, the Sleepiz One+ estimated RR within 3 Brpm in 99.5% of included timepoints.

The two studies evaluated the RR statistics throughout the night, namely the minimum, average, and maximum RR, with the ones obtained from the reference device. The three metrics had an average AE below 1 Brpm in Study 1 and Study 2, indicating that the Sleepiz One+ can accurately measure nightly RR statistics in uncontrolled conditions. This makes the device suitable for its main use scenario, i.e. long-term RR monitoring, that would focus on trend analysis rather than instantaneous RR.

Overall, the two studies yielded similar performance metrics (Table 3) and subgroup analysis showed that there is no notable performance variation between the different diagnosed medical conditions (Table 4). These results provide positive evidence for the accuracy of non-contact respiration monitoring throughout the night in subjects suspected or diagnosed with various sleep-related respiratory disorders, although no further assessment of the comorbidities and their severity was made.

The long-term monitoring of RR in a natural setting enables healthcare professionals (HCPs) to track a patient's health status over time, which empowers them to take decisions on when the patient should visit the hospital or undergo certain diagnostic procedures. There are other devices in the clinical practice able to measure RR, such as the reference devices used in this study, i.e. TREB and nasal cannula. However, these are not apt for long-term monitoring. There are other certified medical devices that measure RR in a contactless fashion, similar to the Sleepiz One+. These are the RSpot 100 (Kai Medical), SleepMinder (now S+) (Biancamed/Resmed), C100 (Circardia) andXK300 (Xandar Kardian)). The performance of these devices with respect to instantaneous RR estimation is comparable to the one obtained by the Sleepiz One+ (Table 5).

The Sleepiz One+ presents several benefits with respect to the solutions available in clinical practice. Firstly, the Sleepiz One+ improves patient comfort during the monitoring of respiratory parameters. Devices such as thoracic respiratory effort bands and nasal cannulas are intrusive and inconvenient. Secondly, the improved comfort, enables long-term respiration monitoring that provides HCPs a more comprehensive picture of the patient's status and enables them to assess trends and changes. Thirdly, the Sleepiz One+ can be used in a natural setting, as opposed to when contact with the device is required. Outcomes in artificial environments may differ from those obtained in natural ones. Thus, the Sleepiz One+ may provide a more accurate picture of the patient's status. Finally, HCPs benefit from the convenience and ease of use of the device; minimal involvement is required from their side, and patients can be remotely monitored.

Therefore, we assume that the Sleepiz One+ performance is sufficient for RR monitoring in patients with chronic lung diseases such as COPD, CF or pulmonary fibrosis. Furthermore, the presented results were obtained on patients with disturbed breathing, confirming the device is suitable to be used on patients with a higher likelihood of irregular breathing.

A study by Wilkens et al.32 demonstrated that common lung diseases have characteristic breathing patterns that allow early diagnosis. Their change could serve the early detection of possible exacerbations. Cloud-based approaches of telemedicine promise improved patient outcomes, increased physician efficiency and decreased costs33. For example, long-term RR monitoring of COPD patients would be helpful as increased RR is a known predictor for exacerbation and hospitalization34. Thus, intervention can be provided earlier, and hospitalization times reduced.

In an older study, the accuracy of the non-contact radiofrequency-based biomotion sensor SleepMinder (ResMed Sensor Technologies, Ireland) was investigated in 20 COPD patients7. It was observed that SleepMinder estimated RR with good accuracy and therefore is suitable for long-term monitoring providing minimal patient effort. Furthermore, Diraco et al. analysed an ultrawideband radar sensor system for continuous in-home monitoring of 30 older adults35. The authors found good overall accuracies between 86 and 96% with a strong dependence on distance and body position. When patients were moving, for example turning around, the accuracy was low due to motion artefacts. Recently, another dual-pulse Doppler system-based approach for the estimation of HR and RR for 20 patients with congestive HF was analysed36. The RR estimation median accuracy was 92%, with a median error of 1.2 Brpm. Using a different method, a video-based approach for RR detection relying on automatic region of interest detection for vital sign monitoring was examined37. The authors created a benchmark dataset of 148 video sequences obtained on adults under challenging conditions and on neonates, suggesting that video respiratory monitoring provides an encouraging measurement option.

To date, others have proposed radar-technology for vital signs monitoring, showing that the approach yields good results38,39,40,41,42. However, these studies are usually done on small patient populations7,38,41,43, and how performance varies across patient demographics is not reported.

In this work, we measure the performance of the Sleepiz One+ in a large patient population, reflective of the device's intended use, and show how the performance is above to that presented in available work39,42 and independent of the patients’ pre-existing conditions, such as sleep apnea severity, COPD, CVD, etc.

The results gained with the Sleepiz One+ device are in line with previous findings, indicating that this device is suitable for long time monitoring of RR without impeding patients’ comfort.

The Sleepiz One+ is intended to be used at the patients’ homes, in addition to the clinic. In the clinic, HCPs are in charge of device setup, while at home the patient is responsible for this task. The effect this has on device performance should be addressed through usability studies where the target users of the device operate it.

Another limitation of conducting the study in a clinical environment and with a PSG as a reference is that participants were subjected to an uncomfortable sleep environment. The Sleepiz One+ , given its contactless nature, allows for natural sleep. We expect device performance to be better in the latter conditions. Due to a reduced frequency of the patients’ movement throughout the night when they sleep in a more comfortable and familiar environment. Whenever patients present significant body movement, the Sleepiz One+ is not able to estimate RR. Thus, at-home measurements have the potential to present a better coverage of the night when RR is available.

To reduce confounding variables, such as apnea events and movement artifacts, a 20-min sleep window within the first 3 h of recording, has been selected to validate the instantaneous RR in Study 1. While this analysis window selection was not related to the subjects’ sleep stage (sleep or awake), it indeed depended on meeting pre-defined inclusion criteria, established to identify 20-min of favourable conditions However, in Study 2, the whole night recordings were analysed to evaluate the instantaneous RR in uncontrolled conditions, which proves the ability of the device to monitor RR during sleep.

A limitation intrinsic to the Sleepiz One+ is its inability to report RR in the presence of significant movement. This could have negative implications for the computation of RR statistics. In this study, RR statistics were computed for TREBs on all epochs where it presented no artifacts, independently of whether the Sleepiz One+ reported a RR estimate at those epochs. In patients with significantly higher variability in RR, a high proportion of body movement could lead to inaccurate RR statistics as estimated by the Sleepiz One+, due to RRs in those epochs not being included in their computation. However, this was not observed in the present study, where the average absolute error of RR statistics was below 1 Brpm (Table 3). Thus, the absence of RR estimates in the presence of tossing and turning does not bias results.

The study compared the instantaneous RR and RR statistics between the Sleepiz device and TREB during a 20-min sleep window under controlled conditions and in a more natural setting during a whole night. The results provide evidence for the effectiveness of the Sleepiz device in estimating the instantaneous RR and its summary statistics in the clinical environment. Future studies are planned on evaluating its performance in the home environment and for long-term patient monitoring.

The data that support the findings of this study are available from the corresponding author upon request.

Absolute error

Breaths per minute

Chronic obstructive pulmonary disease

Heart failure

Limit of agreement

Mean absolute error

Polysomnography

Respiratory rate

Heart rate

Thoracic respiratory effort belt

WHO. The top 10 causes of death. 2020. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death. Accessed 13 Aug 2021.

Fieselmann, J. F., Hendryx, M., Helms, C. M. & Wakefield, D. S. Respiratory rate predicts cardiopulmonary arrest for internal medicine lnpatients. J. Gen. Intern. Med. 8(7), 354–360 (1993).

Article CAS PubMed Google Scholar

Soriano, J. B. et al. Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir. Med. 8(6), 585–596. https://doi.org/10.1016/S2213-2600(20)30105-3 (2020).

Article Google Scholar

Wedzicha, J. A. & Seemungal, T. A. COPD exacerbations: Defining their cause and prevention. Lancet 370(9589), 786–796. https://doi.org/10.1016/S0140-6736(07)61382-8 (2007).

Article PubMed PubMed Central Google Scholar

Yañez, A. M. et al. Monitoring breathing rate at home allows early identification of COPD exacerbations. Chest 142(6), 1524–1529. https://doi.org/10.1378/chest.11-2728 (2012).

Article PubMed Google Scholar

Borel, J. C. et al. Parameters recorded by software of non-invasive ventilators predict copd exacerbation: A proof-of concept study. Thorax 70(3), 284–285. https://doi.org/10.1136/thoraxjnl-2014-206569 (2015).

Article PubMed Google Scholar

Ballal, T. et al. A pilot study of the nocturnal respiration rates in COPD patients in the home environment using a non-contact biomotion sensor. Physiol. Meas. 35(12), 2513–2527. https://doi.org/10.1088/0967-3334/35/12/2513 (2014).

Article PubMed Google Scholar

Savarese, G., Lund, L. H., Savarese, G. & Lund, L. H. Global public health burden of heart failure. Cardiol. Fail. Rev. 4(3), 185. https://doi.org/10.15420/cfr.2016:25:2 (2017).

Article Google Scholar

Saner, H. et al. Case report: Ambient sensor signals as digital biomarkers for early signs of heart failure decompensation. Front. Cardiovasc. Med. 8(February), 1–6. https://doi.org/10.3389/fcvm.2021.617682 (2021).

Article ADS Google Scholar

Churpek, M. M. et al. Derivation of a cardiac arrest prediction model using ward vital signs. Crit. Care Med. 40(7), 2102–2108. https://doi.org/10.1097/CCM.0b013e318250aa5a (2012).

Article PubMed PubMed Central Google Scholar

Massaroni, C., Nicolò, A., Schena, E. & Sacchetti, M. Remote respiratory monitoring in the time of COVID-19. Front. Physiol. 1, 635. https://doi.org/10.3389/fphys.2020.00635 (2020).

Article Google Scholar

Nicolò, A., Massaroni, C., Schena, E. & Sacchetti, M. The importance of respiratory rate monitoring: From healthcare to sport and exercise. Sensors (Switzerland) 20(21), 1–45. https://doi.org/10.3390/s20216396 (2020).

Article Google Scholar

Dafna, E., Rosenwein, T., Tarasiuk, A. & Y. Zigel. Breathing rate estimation during sleep using audio signal analysis. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015-Novem:5981–5984 (2015). https://doi.org/10.1109/EMBC.2015.7319754.

Bae, M., Lee, S. & Kim, N. Development of a robust and cost-effective 3D respiratory motion monitoring system using the kinect device: Accuracy comparison with the conventional stereovision navigation system. Comput. Methods Programs Biomed. 160, 25–32. https://doi.org/10.1016/j.cmpb.2018.03.027 (2018).

Article PubMed Google Scholar

Kim, J., Kwon, J. H., Kim, E., Yoo, S. K. & Shin, C. Respiratory measurement using infrared thermography and respiratory volume monitor during sedation in patients undergoing endoscopic urologic procedures under spinal anesthesia. J. Clin. Monit. Comput. 33(4), 647–656. https://doi.org/10.1007/s10877-018-0214-4 (2019).

Article PubMed Google Scholar

Kuo, Y. M., Lee, J. S. & Chung, P. C. A visual context-awareness-based sleeping-respiration measurement system. IEEE Trans. Inf. Technol. Biomed. 14(2), 255–265. https://doi.org/10.1109/TITB.2009.2036168 (2010).

Article PubMed Google Scholar

Li, M. H., Yadollahi, A. & Taati, B. Noncontact vision-based cardiopulmonary monitoring in different sleeping positions. IEEE J. Biomed. Health Inform. 21(5), 1367–1375. https://doi.org/10.1109/JBHI.2016.2567298 (2017).

Article PubMed Google Scholar

Li, M. H., Yadollahi, A. & Taati, B. A non-contact vision-based system for respiratory rate estimation. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014:2119–2122 (2014). https://doi.org/10.1109/EMBC.2014.6944035.

Ben-Ari, J., Zimlichman, E., Adi, N. & Sorkine, P. Contactless respiratory and heart rate monitoring: Validation of an innovative tool. J. Med. Eng. Technol. 34(7–8), 393–398. https://doi.org/10.3109/03091902.2010.503308 (2010).

Article CAS PubMed Google Scholar

Bennett, M. K., Shao, M. & Gorodeski, E. Z. Home monitoring of heart failure patients at risk for hospital readmission using a novel under-the-mattress piezoelectric sensor: A preliminary single centre experience. J. Telemed. Telecare 23(1), 60–67. https://doi.org/10.1177/1357633X15618810 (2017).

Article PubMed Google Scholar

Breteler, M. J. M. et al. Vital signs monitoring with wearable sensors in high-risk surgical patients a clinical validation study. Anesthesiology 3, 424–439. https://doi.org/10.1097/ALN.0000000000003029 (2020).

Article Google Scholar

Chinoy, E. D. et al. Performance of seven consumer sleep-tracking devices compared with polysomnography. Sleep 44, 5. https://doi.org/10.1093/sleep/zsaa291 (2021).

Article Google Scholar

Tal, A., Shinar, Z., Shaki, D., Codish, S. & Goldbart, A. Validation of contact-free sleep monitoring device with comparison to polysomnography. J. Clin. Sleep Med. 13(3), 517–522. https://doi.org/10.5664/jcsm.6514 (2017).

Article PubMed PubMed Central Google Scholar

Gu, C. Short-range noncontact sensors for healthcare and other emerging applications: A review. Sensors (Switzerland) 16, 8. https://doi.org/10.3390/s16081169 (2016).

Article Google Scholar

de Chazal, P. et al. Sleep/wake measurement using a non-contact biomotion sensor. J. Sleep Res. 20(2), 356–366. https://doi.org/10.1111/j.1365-2869.2010.00876.x (2011).

Article PubMed Google Scholar

Fox, N. A., Heneghan, C., Gonzalez, M., Shouldice, R. B. & de Chazal, P. An Evaluation of a Non-contact Biomotion Sensor with Actimetry. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug. 2007(February): 2664–2668. https://doi.org/10.1109/IEMBS.2007.4352877.

Hong, H. et al. Noncontact sleep stage estimation using a CW Doppler radar. IEEE J. Emerg. Sel. Top. Circ. Syst. https://doi.org/10.1109/JETCAS.2017.2789278 (2018).

Article Google Scholar

Kagawa, M., Suzumura, K. & Matsui, T. Sleep stage classification by non-contact vital signs indices using Doppler radar sensors. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Oct. 2016, 2016-Octob:4913–4916. https://doi.org/10.1109/EMBC.2016.7591829.

Boric-Lubecke, O., Lubecke, V. M., Droitcour, A. D., Park, B.-K. & Singh, A. Doppler Radar Physiological Sensing (Wiley Series in Biomedical Engineering and Multi-Disciplinary Integrated Systems) (Wiley, 2015).

Google Scholar

Skolnik, M. I. Radar Handbook, 53. 1990.

Drummond, G. B., Fischer, D. & Arvind, D. K. Current clinical methods of measurement of respiratory rate give imprecise values. ERJ Open Res. 6(3), 00023–02020. https://doi.org/10.1183/23120541.00023-2020 (2020).

Article PubMed PubMed Central Google Scholar

Wilkens, H. et al. Breathing pattern and chest wall volumes during exercise in patients with cystic fibrosis, pulmonary fibrosis and COPD before and after lung transplantation. Thorax 65(9), 808–814. https://doi.org/10.1136/thx.2009.131409 (2010).

Article CAS PubMed Google Scholar

Sherling, D. & Sherling, M. The promises and pitfalls of telemedicine. Am. J. Account. Care 5(2), 24–26 (2017).

Google Scholar

Franciosi, L. et al. Markers of exacerbation severity in chronic obstructive pulmonary disease. Respir. Res. (2016). https://reference.medscape.com/medline/abstract/16686949. Accessed 15 Oct 2021.

Diraco, G., Leone, A. & Siciliano, P. A radar-based smart sensor for unobtrusive elderly monitoring in ambient assisted living applications. Biosensors (Basel) 7, 4. https://doi.org/10.3390/bios7040055 (2017).

Article Google Scholar

Tran, V. P. & Ali Al-Jumaily A. Non-contact dual pulse Doppler system based respiratory and heart rates estimation for CHF patients. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Aug. 2015:4202–4205. https://doi.org/10.1109/EMBC.2015.7319321.

Janssen, R., Wang, W., Moço, A. & de Haan, G. Video-based respiration monitoring with automatic region of interest detection. Physiol. Meas. 37(1), 100–114. https://doi.org/10.1088/0967-3334/37/1/100 (2015).

Article PubMed Google Scholar

Droitcour, A. D., Boric-Lubecke, O., Lubecke, V. M., Lin, J. & Kovacs, G. T. A. Range correlation and performance benefits in single-chip silicon doppler radars for noncontact cardiopulmonary monitoring. IEEE Trans. Microw. Theory Tech. 52(3), 838–848. https://doi.org/10.1109/TMTT.2004.823552 (2004).

Article ADS Google Scholar

Panzik, A. Society. IEEE; 2009. p. 4812–5. 6. Panzik A. 510k K200445 The Circadia C100 System. 2019. 510k K200445.

Bob, N. Substantial Equivalence Study for Kai Medical Non-Contact Respiratory Rate Monitor.

Lee, Y. J. et al. Validation study of a contactless monitoring device for vital signs during sleep and sleep architecture in adults with sleep-disordered breathing. Sleep Med. Res., 12(2), 118–124 (2021). https://www.embase.com/search/results?subaction=viewrecord&id=L2016570696&from=export.

Kang, S. et al. Validation of noncontact cardiorespiratory monitoring using impulse-radio ultra-wideband radar against nocturnal polysomnography. Sleep Breath. https://doi.org/10.1007/s11325-019-01908-1 (2019).

Article PubMed Google Scholar

Tran, V. P. & Al-Jumaily, A. A. Non-contact dual pulse Doppler system based respiratory and heart rates estimation for CHF patients (2015). https://doi.org/10.1109/EMBC.2015.7319321.

Lauteslager, T. et al. Validation of a new contactless and continuous respiratory rate monitoring device based on ultra-wideband radar technology. Sensors 21, 12. https://doi.org/10.3390/s21124027 (2021).

Article Google Scholar

Shouldice, R. B. et al. Real time breathing rate estimation from a non contact biosensor. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Aug. 2010:630–633. https://doi.org/10.1109/IEMBS.2010.5627275.

Droitcour, A. D. et al. Non-contact respiratory rate measurement validation for hospitalized patients. In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Sep. 2009:4812–4815. https://doi.org/10.1109/IEMBS.2009.5332635.

Rullan, M., Dash, S. S., Wilhelm, E. & Riener, R. Contactless monitoring of breathing rate improves measurement robustness; Abstracts of the 24th Congress of the European Sleep Research Society, 25–28 September 2018, Basel, Switzerland. J. Sleep Res. 27, e12751. https://doi.org/10.1111/jsr.12751 (2018).

Article Google Scholar

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The authors would like to thank the staff at the Sleep lab at Ruhrlandklinik Essen (University hospital Essen, West German Lung Center, Essen, Germany) and the team of Clinic Lengg (Zurich, Switzerland) for their support in conducting the study.

Klinik Lengg AG, Neurorehabilitation Center, Bleulerstrasse 60, 8008, Zurich, Switzerland

Bartosz Bujan

Essen University Hospital, Ruhrlandklinik, Tueschener Weg 40, 45239, Essen, Germany

Tobit Fischer, Sarah Dietz-Terjung, Martina Große Sundrup & Christoph Schöbel

Klinik Lengg AG, Swiss Epilepsy Center, Bleulerstrasse 60, 8008, Zurich, Switzerland

Aribert Bauerfeind & Janne Hamann

Essen University Hospital, Neurorehabilitation Center, Bleulerstrasse 60, 8008, Zurich, Switzerland

Piotr Jedrysiak

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Conception and design: B.B., C.S., J.H., S.D.T. Administrative support: B.B., C.S., M.G.S., P.J., S.D.T., T.F. Provision of study materials or patients: B.B., P.J., A.B., C.S., M.G.S., S.D.T., T.F. Collection and assembly of data: B.B., A.B., C.S., M.G.S., P.J., S.D.T. Data analysis and interpretation: B.B., M.G.S., S.D.T., C.S. Manuscript writing: all authors. Final approval of manuscript: all authors.

Correspondence to Bartosz Bujan.

The study is sponsored by Sleepiz AG. The funds to finance this study come from the 2019 European Institute of Technology (EIT) Venture Award (European Grant). CS and AB as medical advisor with institutional financial support for conducting clinical trials according to European Medical Device Regulation (MDR). BB, PJ, JH, TF, MGS und SDT declare that no conflict of interest exists.

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Bujan, B., Fischer, T., Dietz-Terjung, S. et al. Clinical validation of a contactless respiration rate monitor. Sci Rep 13, 3480 (2023). https://doi.org/10.1038/s41598-023-30171-4

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Received: 03 June 2022

Accepted: 16 February 2023

Published: 01 March 2023

DOI: https://doi.org/10.1038/s41598-023-30171-4

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Clinical validation of a contactless respiration rate monitor