Chad C. Hagen

Comparison of hypopnea definitions in lean patients with known obstructive sleep apnea hypopnea syndrome (OSAHS).

Study objectivesIn the interest of improving inter-rater reliability and standardization between sleep laboratories, hypopnea definitions were recently changed to place less emphasis on arousal scoring and more emphasis on oxygen desaturations. We sought to determine whether these changes would affect detection and treatment of OSAHS in lean patients—a group known to desaturate less-than-obese patients.MethodsThirty-five lean subjects (15 male, 20 women, five post-menopausal) diagnosed OSAHS and a documented benefit from treatment had diagnostic polysomnograms (PSG) originally scored using the American Academy of Sleep Medicine (AASM) rule from 1999 (referred to as “Rule C”). These patients had appropriate clinical care based on those results. PSG records were then re-scored in a randomized and blinded fashion utilizing hypopnea Rule A and B of the 2007 AASM guidelines.ResultsBaseline mean (SD) apnea hypopnea indices (AHI) for rules A, B, and C were 6.4 (3.1), 20.6 (8.2), and 26.9 (7.3), respectively (p < 0.0001). Mean (SD) BMI was 24.4 (1.0). By design, all subjects were treatment responders. Eighty-six percent with CPAP, 83% with oral appliance, and 100% with surgical intervention reported resolution of their initial daytime or sleep complaint. Post-treatment AHIs for rules A, B, and C were 0.8 (0.9), 1.8 (1.2) and 2.3 (1.6; p < 0.001). In all three scoring conditions, the AHI was reduced significantly with treatment (p < 0.001). A repeated measures ANOVA of the difference between scoring methods indicated statistically significant differences between all three strategies at both pre- and post-treatment (p < 0.001). Sleepiness on the Epworth sleepiness scale decreased from a mean of 10.9 (2.3) to 5.7 (1.3) with treatment (p < 0.001). This change in subjective rating of sleepiness was more strongly correlated with rules B and C (r = 0.6) and more modestly correlated with Rule A scoring (r = 0.4).ConclusionResponse to treatment was more tightly correlated with arousal based scoring rules B and C in this group of lean subjects. The1999 hypopnea rule was used at baseline to detect this cohort of patients with OSAHS that ultimately benefitted from treatment. Rule B detected OSAHS and correlated well with response to treatment, but many more were categorized as mild (5 < AHI < 15) at baseline. Since 40% of the subjects had an AHI less than 5 with Rule A, lack of sensitivity should be considered before applying Rule A to the scoring of sleep studies in lean patients.

Classification of breathing events using load cells under the bed

Sleep disturbances are prevalent, financially taxing, and have a negative effect on health and quality of life. One of the most common sleep disturbances is obstructive sleep apnea-hypopnea syndrome (OSAHS) which frequently goes undiagnosed. The gold standard for diagnosing OSAHS is polysomnography (PSG)–a procedure that is inconvenient, time-consuming, and interferes with normal sleep patterns. We are investigating an alternative to PSG in which unobtrusive load cells fitted under the bed are used to monitor movement, heart rate, and respiration. In this paper we describe how load cell data can be used to distinguish between clinically relevant disordered breathing (apneas and hypopneas) and normal respiration. The method correctly classified disordered breathing segments with a sensitivity of 0.77 and a specificity of 0.91.

Classification of lying position using load cells under the bed

Individuals who suffer from acid reflux at night, who snore chronically, or who have sleep apnea are frequently encouraged to sleep in a particular lying position. Side sleeping decreases the frequency and severity of obstructive respiratory events (e.g. apnea and hypopnea) in patients with positional sleep apnea. It has been suggested that individuals with Gastroesophageal Reflux Disease sleep on their left sides in order to help minimize symptoms. In this paper, we present a method of predicting the position of an individual lying on the bed using load cells placed under each of the bed supports. Our results suggest that load cells utilized in this manner could be successfully implemented into a system that tracks or helps train individuals to sleep in a particular lying position.

Accurate scoring of the apnea–hypopnea index using a simple non‐contact breathing sensor

Sleep apnea is a serious condition that afflicts many individuals and is associated with serious health complications. Polysomnography, the gold standard for assessing and diagnosing sleep apnea, uses breathing sensors that are intrusive and can disrupt the patients sleep during the overnight testing. We investigated the use of breathing signals derived from non‐contact force sensors (i.e. load cells) placed under the supports of the bed as an alternative to traditional polysomnography breathing sensors (e.g. nasal pressure, oral‐nasal thermistor, chest belt and abdominal belt). The apnea–hypopnea index estimated using the load cells was not different than that estimated using standard polysomnography leads (t44 = 0.37, P = 0.71). Overnight polysomnography sleep studies scored using load cell breathing signals had an intra‐class correlation coefficient of 0.97 for the apnea–hypopnea index and an intra‐class correlation coefficient of 0.85 for the respiratory disturbance index when compared with scoring using traditional polysomnography breathing sensors following American Academy of Sleep Medicine guidelines. These results demonstrate the feasibility of using unobtrusive load cells installed under the bed to measure the apnea–hypopnea index.

Unobtrusive classification of sleep and wakefulness using load cells under the bed

Poor quality of sleep increases the risk of many adverse health outcomes. Some measures of sleep, such as sleep efficiency or sleep duration, are calculated from periods of time when a patient is asleep and awake. The current method for assessing sleep and wakefulness is based on polysomnography, an expensive and inconvenient method of measuring sleep in a clinical setting. In this paper, we suggest an alternative method of detecting periods of sleep and wake that can be obtained unobtrusively in a patients own home by placing load cells under the supports of their bed. Specifically, we use a support vector machine to classify periods of sleep and wake in a cohort of patients admitted to a sleep lab. The inputs to the classifier are subject demographic information, a statistical characterization of the load cell derived signals, and several sleep parameters estimated from the load cell data that are related to movement and respiration. Our proposed classifier achieves an average sensitivity of 0.808 and specificity of 0.812 with 90% confidence intervals of (0.790, 0.821) and (0.798, 0.826), respectively, when compared to the “gold-standard” sleep/wake annotations during polysomnography. As this performance is over 27 sleep patients with a wide variety of diagnosis levels of sleep disordered breathing, age, body mass index, and other demographics, our method is robust and works well in clinical practice.

Management of Snoring and Obstructive Sleep Apnea with Mandibular Repositioning Appliances: A Prosthodontic Approach

Dentists are becoming increasingly aware of the importance of the detection and management of obstructive sleep apnea. The anatomic and neuromuscular risk factors in the pathogenesis of obstructive sleep apnea are reviewed with particular emphasis on oral findings. Mandibular repositioning appliances hold an important role in the treatment of this condition; however, knowledge of indications and contraindications for treatment, potential areas of oropharyngeal obstruction, appliance design, and treatment steps are vital to ensure maximum treatment success. A review of the steps involved in treatment and management with particular emphasis on collaborative care with physicians is presented.

Cyanotic breath-holding spells in children respond to adenotonsillectomy for sleep-disordered breathing

Children with breath‐holding (BH) spells may demonstrate sleep‐disordered breathing (SDB) during polysomnography. We studied five young children with cyanotic spells retrospectively and found both SDB and a response to adenotonsillectomy. We therefore proceeded with a prospective investigation of treatment for SDB in children with comorbid cyanotic spells. Nineteen children with cyanotic BH spells were identified and enrolled in the prospective study. Parents chose either treatment or observation. Fourteen children underwent complete SDB evaluation and treatment trials while five selected observation only (control group). Sleep and sleep‐surgery specialist evaluation and polysomnography revealed the presence of a narrow upper‐airway and an abnormal respiratory disturbance index in all 14 children. Nasal CPAP was not successful, but adenotonsillectomy performed near 14 months of age eliminated SDB. BH spells were eliminated 1 month after surgery, while they persisted to the end of the study (24 months of age) in the control group. In conclusion, the presence of cyanotic BH should prompt investigation and polysomnography for possible SDB. Independent treatment of SDB may hasten resolution of BH spells in these cases.

A time-frequency respiration tracking system using non-contact bed sensors with harmonic artifact rejection

Sleep apnea is a breathing disorder that affects many individuals and has been associated with serious health conditions such as cardiovascular disease. Clinical diagnosis of sleep apnea requires that a patient spend the night in a sleep clinic while being wired up to numerous obtrusive sensors. We are developing a system that utilizes respiration rate and breathing amplitude inferred from non-contact bed sensors (i.e. load cells placed under bed supports) to detect sleep apnea. Multi-harmonic artifacts generated either biologically or as a result of the impulse response of the bed have made it challenging to track respiration rate and amplitude with high resolution in time. In this paper, we present an algorithm that can accurately track respiration on a second-by-second basis while removing noise harmonics. The algorithm is tested using data collected from 5 patients during overnight sleep studies. Respiration rate is compared with polysomnography estimations of respiration rate estimated by a technician following clinical standards. Results indicate that certain subjects exhibit a large harmonic component of their breathing signal that can be removed by our algorithm. When compared with technician transcribed respiration rates using polysomnography signals, we demonstrate improved accuracy of respiration rate tracking using harmonic artifact rejection (mean error: 0.18 breaths/minute) over tracking not using harmonic artifact rejection (mean error: -2.74 breaths/minute).