Respiratory rate variability in sleeping adults without obstructive sleep apnea

Gutiérrez, Guillermo; Williams, Jeffrey; Alrehaili, Ghadah; McLean, Anna; Pirouz, Ramin; Amdur, Richard L.; Jain, Vivek; Ahari, Jalil; Bawa, Amandeep; Kimbro, Shawn

doi:10.14814/phy2.12949

Cited by 70 publications

(43 citation statements)

References 36 publications

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“…The concomitant measure of other vital signs is also beneficial, as in the development of early warning scores for the prediction of clinical deterioration [4]. Besides, f R and its variability change across wakefulness and different sleep stages [300,301], which is relevant when interpreting nocturnal f R values. Sleep stages are usually identified with electroencephalography, but approaches based on breathing sound processing have also been proposed [81].…”

Section: Perspectives and Challenges Of Respiratory Rate Monitoringmentioning

confidence: 99%

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

Nicolò

Massaroni

Schena

et al. 2020

Sensors

262

137

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Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal.

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Section: Perspectives and Challenges Of Respiratory Rate Monitoringmentioning

confidence: 99%

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

Nicolò

Massaroni

Schena

et al. 2020

Sensors

262

137

View full text Add to dashboard Cite

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“…To do so, values were computed every 15 seconds (on 30-second sliding windows) on DH data and compared to the respective PSG values using an average of the Mean Absolute Error (MAE) computed for each record. An analogous method was employed to assess the capacity of the DH to retrieve respiration rate variability (RRV, in percentage), as described in (25).…”

Section: Assessing Heart Rate Breathing Frequency and Respiration Ramentioning

confidence: 99%

The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging

Thorey²,

et al. 2019

Preprint

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Despite the central role of sleep in our lives and the high prevalence of sleep disorders, sleep is still poorly understood. The development of ambulatory technologies capable of monitoring brain activity during sleep longitudinally is critical to advancing sleep science and facilitating the diagnosis of sleep disorders. We introduced the Dreem headband (DH) as an affordable, comfortable, and user-friendly alternative to polysomnography (PSG). The purpose of this study was to assess the signal acquisition of the DH and the performance of its embedded automatic sleep staging algorithms compared to the gold-standard clinical PSG scored by 5 sleep experts. Thirty-one subjects completed an over-night sleep study at a sleep center while wearing both a PSG and the DH simultaneously. We assessed 1) the EEG signal quality between the DH and the PSG, 2) the heart rate, breathing frequency, and respiration rate variability (RRV) agreement between the DH and the PSG, and 3) the performance of the DH's automatic sleep staging according to AASM guidelines vs. PSG sleep experts manual scoring. Results demonstrate a strong correlation between the EEG signals acquired by the DH and those from the PSG, and the signals acquired by the DH enable monitoring of alpha (r= 0.71 ± 0.13), beta (r= 0.71 ± 0.18), delta (r = 0.76 ± 0.14), and theta (r = 0.61 ± 0.12) frequencies during sleep. The mean absolute error for heart rate, breathing frequency and RRV was 1.2 ± 0.5 bpm, 0.3 ± 0.2 cpm and 3.2 ± 0.6 %, respectively. Automatic Sleep Staging reached an overall accuracy of 83.5 ± 6.4% (F1 score : 83.8 ± 6.3) for the DH to be compared with an average of 86.4 ± 8.0% (F1 score: 86.3 ± 7.4) for the five sleep experts. These results demonstrate the capacity of the DH to both precisely monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for high-quality, large-scale, longitudinal sleep studies. Sleep | EEG | Machine learning | Sleep stages | DeviceCorrespondence: research@dreem.com

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“…Through a visual inspection we can observe that the models seem to focus on two phases of the respiratory cycle, namely plateaus in flow closest to zero between inhalation and exhalation and periods of maximal change in flow rates. Respiratory rate variability [46] and respiratory effort amplitude [47] differ depending on stage of sleep. Thus it is conceivable that the models may be extracting information that approximates respiratory physiology features in trying to classify sleep stage.…”

Section: Flow Signal Saliencymentioning

confidence: 99%

A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging

Aggarwal

Khadanga

Joty

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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Sleep plays a vital role in human health, both mental and physical. Sleep disorders like sleep apnea are increasing in prevalence, with the rapid increase in factors like obesity. Sleep apnea is most commonly treated with Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is no mechanism to monitor a patient's progress with CPAP. Accurate detection of sleep stages from CPAP flow signal is crucial for such a mechanism. We propose, for the first time, an automated sleep staging model based only on the flow signal.Deep neural networks have recently shown high accuracy on sleep staging by eliminating handcrafted features. However, these methods focus exclusively on extracting informative features from the input signal, without paying much attention to the dynamics of sleep stages in the output sequence. We propose an end-toend framework that uses a combination of deep convolution and recurrent neural networks to extract high-level features from raw flow signal with a structured output layer based on a conditional random field to model the temporal transition structure of the sleep stages. We improve upon the previous methods by 10% using our model, that can be augmented to the previous sleep staging deep learning methods. We also show that our method can be used to accurately track sleep metrics like sleep efficiency calculated from sleep stages that can be deployed for monitoring the response of CPAP therapy on sleep apnea patients. Apart from the technical contributions, we expect this study to motivate new research questions in sleep science.

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Respiratory rate variability in sleeping adults without obstructive sleep apnea

Cited by 70 publications

References 36 publications

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging

A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging

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