Contact-free measurement of heart rate, respiration rate, and body movements during sleep

Brink, Mark; Müller, Christopher H.; Schierz, Christoph

doi:10.3758/bf03192806

Cited by 116 publications

(75 citation statements)

References 8 publications

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“…Therefore, researchers have extensively investigated their utility in settings where little movement is observed such as sleep monitoring. In this setting, researchers have developed sensors to incorporate BCG and respiratory measurements on the bed post [3] or mattress [4][15] [16] and have been able to accurately extract vital signs such as HR and BR. Furthermore, this information has been shown to be useful in the detection of circadian rhythms and sleep patterns [15].…”

Section: Introductionmentioning

confidence: 99%

BioWatch: Estimation of Heart and Breathing Rates from Wrist Motions

Hernández¹,

McDuff²,

Picard³

2015

EAI Endorsed Trans Perv Health Tech

105

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Abstract-Continued developments of sensor technology including hardware miniaturization and increased sensitivity have enabled the development of less intrusive methods to monitor physiological parameters during daily life. In this work, we present methods to recover cardiac and respiratory parameters using accelerometer and gyroscope sensors on the wrist. We demonstrate accurate measurements in a controlled laboratory study where participants (n = 12) held three different positions (standing up, sitting down and lying down) under relaxed and aroused conditions. In particular, we show it is possible to achieve a mean absolute error of 1.27 beats per minute (STD: 3.37) for heart rate and 0.38 breaths per minute (STD: 1.19) for breathing rate when comparing performance with FDA-cleared sensors. Furthermore, we show comparable performance with a state-of-the-art wrist-worn heart rate monitor, and when monitoring heart rate of three individuals during two consecutive nights of in-situ sleep measurements.

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Section: Introductionmentioning

confidence: 99%

BioWatch: Estimation of Heart and Breathing Rates from Wrist Motions

Hernández¹,

McDuff²,

Picard³

2015

EAI Endorsed Trans Perv Health Tech

105

View full text Add to dashboard Cite

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“…Consequently, motion signals of the integrated hardware sensors could only be recorded at very restricted, non-constant sampling rates. Although this did not affect the presented classifier, it is a limiting factor for the implementation of more sophisticated algorithms like ballistocardiography [15]. However, as Android is an open platform, this might change in the future.…”

Section: Discussionmentioning

confidence: 99%

Somnography using unobtrusive motion sensors and Android-based mobile phones

Gradl

Leutheuser

Kugler

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-Sleep plays a fundamental role in the life of every human. The prevalence of sleep disorders has increased significantly, now affecting up to 50 % of the general population. Sleep is usually analyzed by extracting a hypnogram containing sleep stages. The gold standard method polysomnography (PSG) requires subjects to stay overnight in a sleep laboratory and to wear a series of obtrusive devices. This work presents an easy to use method to perform somnography at home using unobtrusive motion sensors.Ten healthy male subjects were recorded during two consecutive nights. Sensors from the Shimmer platform were placed in the bed to record accelerometer data, while reference hypnograms were collected using a SOMNOwatch system. A series of filters were used to extract a motion feature in 30 second epochs from the accelerometer signals. The feature was used together with the ground truth information to train a Naive Bayes classifiers that distinguished wakefulness, REM and non-REM sleep. Additionally the algorithm was implemented on an Android mobile phone.Averaged over all subjects, the classifier had a mean accuracy of 79.0 % (SD 9.2%) for the three classes. The mobile phone implementation was able to run in realtime during all experiments. In future this will lead to a method for simple and unobtrusive somnography using mobile phones.

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“…Piezoelectric sensor-based BCG monitoring systems were used for the unconstrained acquisition of N-N intervals. With a growing need for long-term monitoring techniques in sleep studies, ballistocardiography has been suggested as a measure for providing information on cardiac activity without disturbing sleep in an out-of-sleep laboratory environment [13,14,17,18,19,20,21,22,23,24]. Many previous studies validated the usefulness of ballistocardiography for overnight heartbeat monitoring [14,17,18,19,20,22,23]; however, no study has attempted to use ballistocardiographic recording for the unconstrained estimation of the ODI.…”

Section: Discussionmentioning

confidence: 99%

Oxygen Desaturation Index Estimation through Unconstrained Cardiac Sympathetic Activity Assessment Using Three Ballistocardiographic Systems

Jung¹,

Hwang²,

Lee³

et al. 2016

Respiration

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Background: Nocturnal hypoxemia, characterized by abnormally low oxygen saturation levels in arterial blood during sleep, is a significant feature of various pathological conditions. The oxygen desaturation index, commonly used to evaluate the nocturnal hypoxemia severity, is acquired using nocturnal pulse oximetry that requires the overnight wear of a pulse oximeter probe. Objectives: This study aimed to suggest a method for the unconstrained estimation of the oxygen desaturation index. Methods: We hypothesized that the severity of nocturnal hypoxemia would be positively associated with cardiac sympathetic activation during sleep. Unconstrained heart rate variability monitoring was conducted using three different ballistocardiographic systems to assess cardiac sympathetic activity. Overnight polysomnographic and ballistocardiographic recording pairs were collected from the 20 non-nocturnal hypoxemia (oxygen desaturation index <5 events/h) subjects and the 76 nocturnal hypoxemia patients. Among the 96 recording pairs, 48 were used as training data and the remaining 48 as test data. Results: The regression analysis, performed using the low-frequency component of heart rate variability, exhibited a root mean square error of 3.33 events/h between the estimates and the reference values of the oxygen desaturation index. The nocturnal hypoxemia diagnostic performance produced by our method was presented with an average accuracy of 96.5% at oxygen desaturation index cutoffs of ≥5, 15, and 30 events/h. Conclusions: Our method has the potential to serve as a complementary measure against the accidental slip-out of a pulse oximeter probe during nocturnal pulse oximetry. The independent application of our method could facilitate home-based long-term oxygen desaturation index monitoring.

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Contact-free measurement of heart rate, respiration rate, and body movements during sleep

Cited by 116 publications

References 8 publications

BioWatch: Estimation of Heart and Breathing Rates from Wrist Motions

BioWatch: Estimation of Heart and Breathing Rates from Wrist Motions

Somnography using unobtrusive motion sensors and Android-based mobile phones

Oxygen Desaturation Index Estimation through Unconstrained Cardiac Sympathetic Activity Assessment Using Three Ballistocardiographic Systems

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