Sleep/wake measurement using a non-contact biomotion sensor

Chazal, Philip de; Fox, Niall; O’Hare, Emer; Heneghan, Conor; Zaffaroni, Alberto; Boyle, Patricia; Smith, Stephanie; O’Connell, Caroline; McNicholas, Walter T.

doi:10.1111/j.1365-2869.2010.00876.x

Cited by 101 publications

(73 citation statements)

References 34 publications

(56 reference statements)

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“…These muscles maintain a relatively constant level of activity throughout the respiratory cycle (De Chazal et al, 2011). The relevant tongue muscle is the genioglossus, which is referred to as an inspiratory phasic upper airway muscle because its activity is substantially reduced (although not eliminated) during expiration when the pressure inside the airway becomes positive.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the dynamic changes in the soft palate and uvula in obstructive sleep apnea-hypopnea using ultrafast magnetic resonance imaging

Yl¹,

McDonald²,

Yh³

et al. 2014

Genet. Mol. Res.

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ABSTRACT. Apnea and the respiratory cycle are dynamic processes in obstructive sleep apnea-hypopnea (OSAH), which occur only during sleep. Our study aimed to observe the dynamic changes in the soft palate and the uvula during wakefulness and sleep using ultrafast magnetic resonance imaging (UMRI) to provide reference data for the pathogenesis and treatment of OSAH. The dynamic changes in the soft palate and uvular tip of 15 male patients (average age: 50.43 ± 9.82 years) with OSAH were evaluated using UMRI of the upper 8597 Pathogenesis of obstructive sleep apnea-hypopnea ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 13 (4): 8596-8608 (2014) airway while asleep and awake after 1 night of sleep deprivation. A series of midline sagittal images of the upper airway were obtained. The distance from the center of the soft palate to the x-axis (an extended line from the anterior nasal spine to the posterior nasal spine), from the uvular tip to the x-axis, from the center of the soft palate to the y-axis (a perpendicular line from the center of the pituitary to the x-axis), and from the uvular tip to the y-axis (designated as PX, UX, PY, and UY, respectively) were measured during sleep and wakefulness. The minimum PX, PY, UX, and UY were shorter during sleep than during wakefulness, whereas the maxima were longer during sleep (P < 0.01), the differences between the maximum and minimum PX, PY, UX, and UY were larger during sleep (P < 0.01). The upward, downward, forward, and backward ranges of movement of the soft palate and the uvular tip were larger during sleep in OSAH patients. This increased compliance may trigger each airway obstructive event.

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Section: Discussionmentioning

confidence: 99%

Analysis of the dynamic changes in the soft palate and uvula in obstructive sleep apnea-hypopnea using ultrafast magnetic resonance imaging

Yl¹,

McDonald²,

Yh³

et al. 2014

Genet. Mol. Res.

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“…These less obtrusive sensors include amongst others wrist actigraphy (Ancoli et al 2003), radar sensors (De Chazal et al 2011), pressure sensors in the pillow (Harada et al 2000), pressure sensors in the bed sheet (Samy et al 2014), Piezo effect sensors, strain-gauge and electret foil sensors (Aubert and Brauers 2008). Most common are so-called wrist actigraphy devices that are worn around the wrist measuring the subject's activity for deriving sleep/wake episode estimates (Littner et al 2003;Ancoli et al 2003).…”

Section: Introductionmentioning

confidence: 98%

Video based actigraphy and breathing monitoring from the bedside table of shared beds

Heinrich

Heesch

Puvvula³

et al. 2014

J Ambient Intell Human Comput

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Good sleep is an important factor for a high quality of life. Presence of sleep disorders requires patients to undergo polysomnography examinations at a sleep clinic, which involves attaching multiple head and body sensors. This results in an uncomfortable and unnatural sleep setting for the patients, complicating an accurate diagnosis. Currently, this diagnosis is done manually, making the entire process time consuming and cumbersome. We propose a camera based system capable of analyzing the subjects in their own sleeping environment. The system segments the primary subject from the background and any other bed occupants, then computes the actigraphy and the breathing characteristics of the subject. Segmentation is performed with an AdaBoost classifier using among others motion, intensity, focus and Histogram of Oriented Gradients features. A sum of absolute differences operation on the pixels within the segmented area of the primary subject returns the actigraphy signal. The breathing characteristics are extracted based on small motion analysis of consecutive and reference frames. The proposed system has been evaluated on four healthy adults for actigraphy and on five healthy adults for breathing analysis using a Texas Instrument Chronos wrist watch and inductive respiratory belts as references respectively. Evaluation was performed using the metrics accuracy, precision, sensitivity and breathing rate correspondence.The proposed system has an average accuracy of 88 % at a precision of 79 % for segmentation of the primary subject. It can detect movements up to an accuracy of 85 % while outperforming wrist actigraphy at 75 % accuracy. State-ofthe-art video based breathing algorithms were surpassed with an overall sensitivity of 87 %, precision of 90 %, and a breathing rate correspondence of 93 %.

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“…Q UADRATURE microwave radar systems have been proposed for several physiological sensing applications used in both the hospital [1]- [4] and home environments [5]- [7]. Microwave radar records a ballistographic (BG) signal, so it has similar advantages and disadvantages as a BG signal that is recorded with a pressure sensor [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Quadrature Imbalance Compensation With Ellipse-Fitting Methods for Microwave Radar Physiological Sensing

Zakrzewski

Singh

Yavari

et al. 2014

IEEE Trans. Microwave Theory Techn.

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Accurate displacement measurement using quadrature Doppler radar requires amplitudes and phase imbalance compensation. Previously, this imbalance calibration has required cumbersome hardware modifications and thus can only be performed in a laboratory setting. Recently, a data-based method that does not require hardware modifications has been proposed. This simplifies the calibration process and allows the calibration to be performed on-site periodically. The method is called ellipse fitting. In this paper, the different factors affecting imbalance estimation accuracy, namely, arc length, initial phase angle, and noise level were thoroughly investigated. The Levenberg-Marquardt (LM) algorithm is proposed for the first time to increase the estimation accuracy as compared to the previously suggested algebraic fitting. Comprehensive simulations and experimental data show that the algebraic fitting method results in biased estimates. The proposed LM method has also been demonstrated to be more robust to noise, varying arc lengths, and different initial angles. The LM method reaches sufficient imbalance estimation accuracy with an arc length of 40% and a noise level of 1.5%.

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Sleep/wake measurement using a non-contact biomotion sensor

Cited by 101 publications

References 34 publications

Analysis of the dynamic changes in the soft palate and uvula in obstructive sleep apnea-hypopnea using ultrafast magnetic resonance imaging

Analysis of the dynamic changes in the soft palate and uvula in obstructive sleep apnea-hypopnea using ultrafast magnetic resonance imaging

Video based actigraphy and breathing monitoring from the bedside table of shared beds

Quadrature Imbalance Compensation With Ellipse-Fitting Methods for Microwave Radar Physiological Sensing

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