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Keywords:

  • load cells;
  • non-contact sensors;
  • polysomnography;
  • respiratory effort;
  • sleep apnea;
  • unobtrusive sleep monitoring

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. References

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 patient's 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, = 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.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. References

Sleep apnea is a condition that has serious health and financial implications. It is estimated that 9% of middle-aged women and 24% of middle-aged men have an apnea–hypopnea index (AHI) of 5 or greater (Young et al., 1993). Obstructive sleep apnea is associated with several cardiovascular complications, such as hypertension, myocardial dysfunction, coronary artery disease and cardiac arrhythmias (Redline et al., 2007). Sleep apnea may also be associated with depression, have some neuropsychological effects, lead to structural changes in the brain and have some impact on one's quality of life (Baldwin et al., 2001; Beebe et al., 2003; Joo et al., 2010; Macey et al., 2008; Peppard et al., 2006; Yang et al., 2000). Individuals with obstructive sleep apnea may also have a higher risk of being involved in a motor-vehicle collision (Sassani et al., 2004). Polysomnography (PSG) is the traditional standard for diagnosing and monitoring individuals with sleep apnea; however, the nasal pressure cannulas, oral thermistor, chest belt and abdomen belt used to detect patient breathing are obtrusive. Furthermore, maintaining the correct placement with these leads can sometimes be problematic. Sleep architecture, sleep efficiency, rapid eye movement (REM) latency and duration, and electroencephalogram (EEG) spectral power are altered during the first night or first few nights of PSG (Agnew et al., 1966; Curcio et al., 2004). This first night effect has been suggested to be caused in part by the discomfort and restricted movement resulting from numerous leads placed on the patient (Le Bon et al., 2001). Therefore, a less obtrusive method for detecting accurate respiration information is of interest.

One alternative to body-worn leads is the use of load cells (LCs) installed under the supports of the bed (Brink et al., 2006; Paalasmaa et al., 2011). LCs are force sensors that continuously and independently measure the weight supported by each leg of the bed. The LCs are sensitive enough to pick up small fluctuations in weight at the various bed supports that result from slight mass displacements on the bed due to the patient's breathing. In previous work, we have shown that apneic events are distinguishable from normal, quiet breathing using automated classification of segmented LC signals (Beattie et al., 2009). However, it is unknown if the signal from the LCs could be used in the PSG montage to allow visual scoring of American Academy of Sleep Medicine (AASM)-defined apneic and hypopneic events.

We report on the use of LCs in a sleep laboratory to detect sleep apnea. We compare the scoring of routine AASM-compliant PSG data with scoring of the same data with replacement of all flow and effort channels with LC channels.

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. References

The LC system consists of pressure sensors (i.e. LCs) that are placed under the supports of a bed. The LCs detect movement on the bed as fluctuations in the forces supported by each of the bed legs. The signals from the different LCs are combined to create a breathing signal. First, the combination of all movements on the bed can be represented in the LC signals by summing the output of each LC under each support of the bed. Second, the different outputs from each LC can be used to calculate the time-dependent center of pressure, as described in Beattie et al. (2011). This allows the LCs to detect the breathing of an individual on the bed by tracking the changes in the center of mass caused by abdominal organs being displaced by the diaphragm toward the foot of the bed on inspiration and then toward the head of the bed on exhalation (Fig. 1). The LC breathing trace is the result of low-pass filtering the LC center of pressure signal in the y-direction (i.e. along the long axis of the bed). The LC data for this study were collected from LCs that were placed under each of the five supports of a bed at the Pacific Sleep Program sleep lab (Portland, OR, USA).

image

Figure 1. Illustration of how the LCs detect breathing via small mass (M) displacements. As an individual lies on the bed, the LCs detect the forces supported by each bed leg. (a) During inspiration mass is displaced towards the foot of the bed. (b) During expiration mass is displaced towards the head of the bed. (c) An example of a LC breathing signal from a patient is shown. Periods of inspiration are marked in red and periods of expiration are marked in black.

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Patients gave informed written consent to the study (OHSU Institutional Review Board eIRB 6308), and then their overnight PSG data were collected concurrently with their LC data. PSG data were collected using Datalab, Rembrandt 9.0 (Embla, Ottawa, ON, Canada) and initially scored in accordance with current AASM guidelines using Analysis Manager, Rembrandt 9.0 (Embla). An experienced PSG technologist used AASM rules for the scoring of AASM-defined central apnea (CA), mixed apnea, obstructive apnea (OA), and hypopnea. The sums of scored apneas and hypopneas were divided by total sleep time to generate the AHI-PSG. For determining hypopneas, rule IV.A was used (requiring a 4% desaturation). Optional rule IV.C scoring of respiratory effort-related arousals (RERA) was applied to label discernible reductions in airflow associated with arousal that did not meet criteria for other events. The total of these events was combined with the sum of apneas and hypopneas and divided by total sleep time to obtain the respiratory disturbance index (RDI-PSG).

Apnea patients were then classified by their AHI as having negative (AHI < 5), mild (5 ≤ AHI < 15) or moderate–severe (AHI ≥ 15) apnea based on their PSG results. Fourteen records were selected for the negative group, 16 records were selected for the mild group, and 15 records were selected for the moderate–severe group (45 patients in total). The data for all 45 records were anonymized and converted to European Data Format. All channels related to airflow or respiratory effort were removed from the PSG record (nasal pressure, oral-nasal thermistor, chest belt and abdominal belt). The remaining tracings from the routine 16-channel montage were integrated with LC tracings to create a LC montage (Fig. 2).

image

Figure 2. Screen shots comparing the scoring montages used for scoring with typical PSG signals (upper) and with LC breathing signals (lower). The screen shots were taken from the same 120 s for both scoring results from one patient. The nasal pressure, oral-nasal thermistor, chest belt and abdominal belt are colored purple in the PSG scoring montage (upper), and the LC breathing signals are similarly colored purple in the LC scoring montage (lower). The LC tracing ‘All_Sum_HP’ is the summation of all the LCs, and the ‘COP_Y_HP’ is the center of pressure LC signal. The purple, horizontal boxes in both cases indicate the locations of scored respiratory events.

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The same scorer subsequently scored blindly the integrated LC record at least 5 months after scoring the initial PSG, using Analysis Manager Rembrandt 9.0 (Embla 2008). Standard AASM respiratory event scoring rules for routine scoring were applied for duration of event and percentage reduction in the LC tracing excursion. A 30–90% reduction in the LC excursion for greater than 10 s associated with a 4% desaturation was scored as obstructive hypopnea (OH-LC). A reduction of the LC excursion of 90% or greater for more than 10 s was scored as OA-LC. Absence of LC excursion for greater than 10 s duration was scored as CA-LC. LC examples of actual respiratory events from each category (OH-LC, OA-LC and CA-LC) scored by the technologist are shown in Fig. 3. AHI-LC was determined from the number of events (OH-LC, OA-LC and CA-LC) per hour of sleep. Discernible reductions in the excursion for 10 s or greater duration associated with an EEG arousal that did not meet criteria for other events were scored as a RERA (RERA-LC). The number of RERA-LC was summed with the combined number of OH-LC, OA-LC and CA-LC, and this overall total was divided by total sleep time in order to calculate the RDI-LC.

image

Figure 3. Segments of the LC breathing signal from a single patient illustrating the scoring of respiratory events using the LC trace. (Upper) A scored hypopnea (HYP) showing a slight reduction in the excursion of the LC signal. (Middle) A scored obstructive apnea (OA) showing a major reduction in the excursion of the LCs signal. However, some LC excursion appears to still be present, suggesting breathing effort may still exist. (Lower) A scored central apnea (CA) showing a complete absence of excursion in the LC signal.

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Analysis

Differences between AHI severity group characteristics [specifically age, body mass index (BMI) and sex] were examined using a multivariate analysis of variance (manova) with the AHI group as the fixed effect. The dependence of the LC scoring accuracy on BMI was examined using linear regression and a t-statistic to test whether the resulting slope was different from 0. The log-transformed absolute difference between PSG scoring and LC scoring for both AHI and RDI (i.e. scoring error) was regressed against BMI for this analysis. Comparison of the traditional PSG to LC scoring was analysed using linear correlation, a paired t-test and 95% confidence intervals for the difference between the two scorings. Finally, the accuracy of scoring the AHI severity of patients using the LC montage was assessed using sensitivity and specificity.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. References

The overall demographic information and the apnea class-specific demographics are contained in Table 1. Overall, there were significant differences in demographics between the groups (P = 0.02). Post hoc tests revealed that this was due primarily to the younger age of the low AHI group compared with the high AHI group. The intra-class correlation coefficient (ICC) for AHI was 0.97 with a 95% confidence interval of (0.95 0.98); the ICC for RDI was 0.85 with a 95% confidence interval of (0.66 0.93). The AHI estimated by PSG was on average only 0.4 × larger than that estimated using LCs, which was not significant [t44 = 0.37, P = 0.71 with a 95% confidence interval of (−1.67 2.42)]. In contrast, the RDI estimated from the LC montage was on average 7.7 × greater than that obtained using the PSG montage; this difference was significant (t44 = −3.89, P < 0.001) with a 95% confidence interval of (−11.7 −3.70). Although there were differences in the absolute estimates of AHI and RDI, the PSG and LC scoring were strongly correlated for both AHI and RDI (Pearson's correlation coefficient 0.97 and 0.89, respectively). Least square linear fits for AHI and RDI comparing PSG and LC scoring are shown in Fig. 4. Agreement between the two scoring modalities is shown using Bland–Altman plots in Fig. 5.

Table 1. Demographic information for all patients
  Gender (M/F) Age (years) BMI (kg m −2 )
  1. AHI, apnea–hypopnea index; BMI, body mass index.

  2. Values are reported as mean ± standard deviation.

  3. a

    denotes significant difference between the two groups.

AHI < 56/8 43.9 ± 13.6a29.8 ± 7.1
5 ≤ AHI < 1510/6 52.4 ± 13.233.4 ± 6.5
AHI ≥ 1511/4 56.1 ± 13.8a33.7 ± 6.0
Overall27/1851.0 ± 14.232.3 ± 6.6
image

Figure 4. Linear least-squares regression plots for apnea–hypopnea index (AHI)-LC versus AHI-polysomnogram (PSG) and respiratory disturbance index (RDI)-LC versus RDI-PSG.

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image

Figure 5. Bland–Altman plots for visualization of the agreement between the polysomnogram (PSG) and LC scoring of apnea–hypopnea index (AHI) and respiratory disturbance index (RDI).

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The sensitivities and specificities of AHI-LC to detect sleep apnea for various AHI cutoffs are contained in Table 2. The positive likelihood ratios for each AHI cutoff are also presented in Table 2. Our ability to detect severe apnea in particular was very high, with 100% sensitivity and 97% specificity, and with a positive likelihood ratio of 33.

Table 2. Sensitivities, specificities and LR+ of using AHI-LC to detect sleep apnea for several AHI cutoffs
  Sensitivity Specificity LR+
  1. AHI, apnea–hypopnea index; LR+, positive likelihood ratio.

AHI ≥ 50.840.794
AHI ≥ 150.870.9729
AHI ≥ 301.000.9733

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. References

This study showed that using unobtrusive LCs under the bed to replace the four standard respiratory leads for PSG provides an accurate measure of AHI. The AHI using LC respiration tracings was highly correlated with the standard montage. Finally, the LC AHI was also highly predictive of the presence of sleep apnea, particularly for AHIs greater than 15. Our success at detecting mild apnea is encouraging, and we believe LC performance will improve with our future work to clarify how specific features in the LC excursion correlate with various types of respiratory events.

In order to control for inter-rater variability, we chose to have both the standard PSG records and the LC montages scored by the same registered PSG technologist. Thus, each PSG record was scored twice by the same individual. At least 5 months elapsed between the original scoring of the standard PSG record and the anonymized scoring of the LC montage. Consequently, between the 1st and 2nd scoring of each record the technologist scored over 300 PSG records, and prior to scoring the LC montage the records were anonymized, further mitigating any chance that the technologist would be influenced by recollection of the 1st scoring. Thus, there could be no recall of raw data appearance or blinded record labels.

As expected, AHI-LC did very well compared with AHI-PSG. We were not surprised to find that LC scoring tended to be less specific when scoring RERA compared with hypopnea and apnea. RERA scoring has been shown to have lower inter-scorer reliability, to be less sensitive when based on nasal pressure and respiratory inductance plethysmography (Redline et al., 2007), and is best performed with esophageal manometry, which was not used in this protocol. We are encouraged by our success at detecting RERA. Though improvement is needed, our further work analysing feature changes specific to flow reductions with arousal and RERA may permit a non-invasive method for detecting patients with upper airway resistance syndrome that have historically required more invasive tests with esophageal manometry (Tantrakul and Guilleminault, 2009). There has been great interest in finding non-invasive ways to measure RERAs and flow limitations. Other groups have worked on novel methods for identifying respiratory effort (Tenhunen et al., 2011). Flow limitation during total sleep time is also of interest, as flow limitation may impair sleep during each breath rather than with distinct scorable events (Chervin et al., 2012; Tantrakul and Guilleminault, 2009). Additional error in the RDI observed in this study may be due to limitations in the current standard for arousal detection. We followed the current AASM definition for arousal. Changes in the sleep EEG may be better detected by quantified EEG or evaluation of the cyclic alternating pattern (CAP; Guilleminault et al., 2007). A thorough analysis of LCs to detect respiratory-related arousal is beyond the scope of the current paper, though we intend to continue working on enhanced detection of RERAs using LCs.

The LC breathing signal comes from mass movement; therefore, it may actually be more indicative of respiratory effort. However, it could also be argued that the LCs susceptibility to non-respiratory-related movement could have contributed to more RERAs being scored. Similarly, because the LCs are detecting mass movements, we were concerned that higher BMI might make changes in the LC breathing signal less apparent. In fact, the AHI scoring error (i.e. the difference between AHI-PSG and AHI-LC) increased significantly with increasing BMI (t43 = 2.06, P = 0.05), although only 9% of the variance in the AHI scoring error was accounted for by BMI. There was no dependence (t43 = 1.09, P = 0.28) on BMI found for RDI scoring error.

The rules used in this study to identify apneic events were adapted from AASM standards for scoring events using flow and thermistor tracings. As the LC signal originates from visceral mass movements caused by breathing as opposed to actually measuring air flow, it seems obvious that different standards fine-tuned to the nuances of the LC signals themselves would improve the accuracy of using the LC tracings. We are isolating morphological changes that improve event type specificity, and future work includes an event by event analysis to develop LC-specific scoring features. We have previously reported successful discrimination of OA from CA (Beattie et al., 2009). Discriminating central hypopnea from OH may be aided by observation of retention of the normal rounded nasal pressure excursion, absence of paradoxical respiratory effort, absence of snoring, decreased intercostal electromyogram activity and/or less negative pressure swings on esophageal manometry when available. While we demonstrated that CA is reliably identified with LCs in previous work, we believe our future work at isolating the unique morphological changes within the LC signal will help identify reduced effort in more subtle events like central hypopnea and help discriminate these from OH or RERA. In the future, we also plan to refine our ability to detect RERAs with the LC signals, to explore the use of LCs to identify respiratory effort, and to study the analytic correlation between LC recording and CAP scoring in non-REM sleep.

We have demonstrated the feasibility of using LCs installed under the bed to detect sleep apnea. Other unobtrusive devices have been proposed for monitoring sleep apnea. The most studied of these is the static charge sensitive bed (SCSB), which has also been utilized to visually score apneic events (Polo et al., 1988; Salmi et al., 1986). The SCSB is placed under the mattress and detects movement of an individual lying on the bed as changes in static charge (Alihanka et al., 1981). The LC placement under the supports of the bed not only allows the detection of movement such as those associated with breathing, but has several other possible applications not possible with the SCSB. Calibrated LCs can be used to monitor a patient's weight as well as the lying position of an individual lying on the bed (Beattie et al., 2011). LC data are easy to collect, and do not require any sensors in contact with the patient. Current flow and effort leads during attended PSG are obtrusive and disruptive to sleep. Unlike standard PSG leads, the LCs do not become displaced during the night, resulting in more reliable signals and far superior signal integrity for serial night collections such as might be seen during unattended home monitoring. While the study presented herein was performed in a sleep lab, we will soon be placing similar devices in patients' own homes for long-term, unobtrusive monitoring while they sleep in a familiar environment.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. References

This project was supported by Grant Number R01HL098621 from the National Heart, Lung and Blood Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung and Blood Institute or the National Institutes of Health. The authors would like to thank John Hunt M.S.E.E for his technical expertise in developing the load cell system.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. References
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