Chest ACCP Member Benefits
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Guest Access | Sign In via User Name/Password
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF) Free
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Article Archive
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (3)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Kowallik, P.
Right arrow Articles by Wirtz, H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Kowallik, P.
Right arrow Articles by Wirtz, H.
(Chest. 2001;119:451-459.)
© 2001 American College of Chest Physicians

Breath-to-Breath Variability Correlates With Apnea-Hypopnea Index in Obstructive Sleep Apnea*

Peter Kowallik, MD; Ilka Jacobi, MD; Alexander Jirmann, MD; Malte Meesmann, MD; Michael Schmidt, MD and Hubert Wirtz, MD

* From the Department of Medicine (Drs. Kowallik, Jacobi, Jirmann, Meesmann, and Schmidt), University of Würzburg, Würzburg; and the Department of Medicine (Dr. Wirtz), University of Leipzig, Leipzig, Germany.

Correspondence to: Peter Kowallik, MD, Medizinische Universitätsklinik, Josef-Schneider-Str. 2, D-97080 Würzburg, Germany; e-mail: kowallik{at}mail.uni-wuerzburg.de


   Abstract
TOP
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Background: Breathing in patients with obstructive sleep apnea (OSA) is frequently interrupted by periods of hypopnea and apnea. There is limited information regarding a possible disturbance of breathing outside these periods.

Study objective: To analyze the degree of breathing disturbance during nonocclusion.

Design: Prospective determination of breathing variability during full polysomnographic sleep studies.

Patients: Breath-to-breath variation was monitored in 34 patients with OSA and in 9 healthy subjects.

Measurements and results: All breath-to-breath intervals were automatically analyzed from flow signal, displayed, and manually corrected for artifacts. Distribution of all nonapneic breath intervals was analyzed for the extent of difference from a normal distribution pattern by specifying kurtosis. In untreated OSA patients, kurtosis was significantly reduced (0.0 ± 0.5, mean ± SD) compared to control subjects (0.8 ± 0.5), indicating increased variability of nonoccluded breathing. This effect was present in all sleep stages, and the extent depended significantly on the degree of disease. Continuous positive airway pressure breathing was able to normalize kurtosis (1.0 ± 0.9) immediately.

Conclusions: Breathing in OSA is not only characterized by interruptions of breathing during occlusion, but by a greater variation in the pattern of normal-length breaths.

Key Words: airflow obstruction • breathing variability • continuous positive airway pressure • obstructive sleep apnea


   Introduction
TOP
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Sleep -disordered breathing is a widespread disease1 with varying forms of manifestation.2 The most severe form is obstructive sleep apnea (OSA), with daytime sleepiness and associated cardiovascular disease and other sequelae.3 4 5 The underlying condition is increased upper-airway collapsibility for reasons yet to be determined.6 Even if upper-airway obstruction is incomplete, increased upper-airway resistance will still cause clinical symptoms similar to OSA7 because of respiratory effort-related arousals.8 Diagnosis of OSA involves screening as well as respiratory nocturnal polysomnography.9 For detection of upper-airway resistance syndrome (UARS), measurement of esophageal pressure in combination with arousal detection is the "gold standard."10 Both of these diagnostic procedures are time-consuming and require considerable technical expense, while causing patient discomfort. In general, an increase in resistance of the upper airway will lead to changes in the flow contour11 and lengths of the breathing cycle, as well as increasing the variability of breath-cycle length.12 13 Thus, sleep-disordered breathing with the common underlying condition of increased upper-airway resistance may be characterized by a varying degree of breath cycle-length variability different from the normal pattern of breathing.

The purpose of this study was to verify whether or not breath cycle-length variability, readily determined by standard thermistor flow-sensor equipment, is significantly different in OSA patients compared to healthy control subjects, and whether or not it correlates with the extent of OSA estimated by apnea-hypopnea index (AHI).


   Materials and Methods
TOP
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Study Population
Thirty-four patients (age range, 30 to 68 years) were referred to this institution for polysomnographic evaluation because of daytime sleepiness or other sleep apnea-related symptoms (Table 1 ).


View this table:
[in this window]
[in a new window]

  		Table 1. Polysomnographic Sleep Study*

 
Nine healthy volunteers (age range, 22 to 52 years) without a history of daytime sleepiness, and having normal findings on physical examination and functional tests, such as resting ECG, echocardiography, lung function assessed by body plethysmography, and home sleep study (Jäger Apnoe Screen 2 Plus; Jäger-Tönnies; Würzburg, Germany), served as a control group. Control subjects gave informed consent to participate in the study.

Study Protocol
Patients as well as healthy subjects underwent a full-night polysomnographic sleep study. Patients in whom OSA was diagnosed and who received continuous positive airway pressure (CPAP) treatment underwent a second polysomnographic study the next night.

Polysomnography
Polysomnography was performed for 8 h during the night (Sleep Lab 1000 P; Jäger-Tönnies). Fourteen channels were recorded continuously: airflow signal (32-Hz sampling rate) recorded by a nasal/oral thermistor flow sensor (Jäger-Tönnies), two EEG channels, two electromyogram channels, two electro-oculogram channels, one channel recording limb activity, one channel recording body position, two channels recording thoracic and abdominal effort, one channel recording snoring, one channel recording transdermal oxygen saturation, and one ECG channel.

Therapy
Patients with an AHI of > 30, or an AHI > 5 and a history of symptoms typical for sleep apnea, received treatment with CPAP (Somnotron 2; Weinman; Hamburg, Germany) if they consented to this kind of treatment, as recently recommended in a consensus statement.14 A nasal CPAP mask was fitted, and the effective pressure individually determined for each of these seven patients. Starting from a pressure of 5 millibars (5.1 cm H2O), the CPAP pressure was increased by 1 millibar (1.02 cm H2O) whenever there was more than one significant fall in oxygen saturation, apnea, or hypopnea.14 When this did not occur for 1 h, pressure was slowly decreased until desaturations reappeared, in which case pressure was increased again.

Data Analysis
Polysomnographic Sleep Study:
Evaluation of the sleep studies were done according to guidelines.15 16 Sleep stage was defined according to standard criteria15 and grouped into four different levels: awake, rapid eye movement (REM), sleep stages 1 and 2, and sleep stages 3 and 4. Hypopnea was determined as a reduction in the flow signal of > 50%, lasting > 10 s and causing a decrease in the oxygen saturation of > 4%.2 17 18 Apnea was defined as an interruption of the flow signal for a duration of at least 10 s.2 17 The number of apneas and hypopneas were calculated to obtain the AHI. Results of the polysomnographic sleep study are shown in Table 1 .

Flow Signal:
Analysis was performed off-line. Data were exported in raw format and further processed using software specially developed for this study by one of the authors. The digitized flow signal was extracted from the raw data file of the sleep study. Flow-signal minima were identified automatically. The distance between two minima was taken as the duration of one breath. This marker was used instead of zero flow, because mimima were more reliable to detect, compared to the onset of flow. All breath-to-breath intervals were determined and displayed (Fig 1 ). The actual flow signals of all breaths were scanned on a monitor, and artifacts were corrected manually.



View larger version (48K):
[in this window]
[in a new window]
[Download PPT slide]
  		Figure 1. Breath-to-breath intervals of the total recording period for OSA patient 2 (top, A) and OSA patient 12 (bottom, B), treated the first night with nasal CPAP.

 
Distribution of Breath-to-Breath Intervals:
Means and standard deviations of all breath-to-breath intervals < 10 s were calculated (ie, apneic episodes were not included). Furthermore, normal length of breath-to-breath intervals was defined by a duration within ± 50% of the median breath-to-breath interval duration. The distribution of these breath-to-breath intervals of normal length was analyzed for deviation from a normal distribution by specifying its kurtosis.

The kurtosis of a distribution compares the shape of a distribution to a normal distribution (ie, the fact whether the distribution curve is more flat or more pointed, compared to a normal distribution).19 If the distribution is normal, the value of kurtosis is, by definition, zero. A positive value of kurtosis indicates a sharp peak with longer tails including only few cases. That is, individual values are crowded together around the mean. A negative value indicates that the peak is flattened, compared to a normal distribution with many cases widely apart from the mean.

Statistical analysis was performed using commercial software (Systat 7.0.1 for Windows; SPSS; Chicago, IL). Data of measurements for single subjects were expressed as mean ± SD. Data of measurements between groups were expressed as mean ± SEM, and the Mann-Whitney test was used for comparison. A p value of < 0.05 was considered significant.


   Results
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Breathing Pattern in Healthy Subjects
The breathing pattern in healthy subjects was very regular. Apneas did not occur, and the standard deviation for the duration of all nonapneic breaths was small. In addition, the distribution of all breath-to-breath intervals within the normal range (ie, within ± 50% of the median breath-to-breath interval) was very narrow (Fig 2 , left, A). This was expressed by a kurtosis significantly more than zero, indicating that the density distribution of the regular breath-to-breath intervals was more acute than expected for a normal distribution.



View larger version (39K):
[in this window]
[in a new window]
[Download PPT slide]
  		Figure 2. Distribution of all breath-to-breath intervals for control subject 1 (left, A) [kurtosis, 0.62], OSA patient 2 (middle, B) [kurtosis, - 0.67], and OSA patient 12, (right, C) treated the first night with nasal CPAP (kurtosis, 1.20).

 
Breathing Pattern in Untreated OSA Patients
Breathing in OSA patients was characterized by frequent discontinuations. This became obvious in apneic breath-to-breath intervals of > 10 s in the tachogram of all consecutive breath-to-breath intervals (Fig 1 , top, A).

However, nonapneic breathing in these patients was also altered by OSA. The standard deviations of these nonapneic breaths was increased, compared to that of healthy subjects. In addition, the frequency distribution of breath-to-breath intervals of normal length was also different from that of the control subjects. The kurtosis of this distribution was significantly lower compared to that in control subjects (Fig 3 ). This is equivalent to a less-regular breathing pattern, as was obvious in the analysis of breath-to-breath intervals of a single patient over the entire duration of sleep (Fig 2 , middle, B) compared to a healthy subject (Fig 2 , left, A). Compared to the total nighttime analysis, the same decrease in kurtosis was also found in periods that were not interrupted by apneas. One example would be the breath-to-breath intervals of 3,160 to 3,560 of patient 2 (arrows in Fig 1 , top, A) with a kurtosis of - 0.32. Age did not significantly influence kurtosis within the group of OSA patients (Table 2 ).



View larger version (12K):
[in this window]
[in a new window]
[Download PPT slide]
  		Figure 3. Comparison of kurtosis between OSA patients before and with nasal CPAP therapy and healthy subjects (mean ± SEM; *p < 0.01, Mann-Whitney test).

 

View this table:
[in this window]
[in a new window]

  		Table 2. Polysomnographic Sleep Study and Kurtosis By Age*

 
Relation of Regularity of Breathing to Occurrence and Severity of OSA
In patients with OSA, we found a significant positive correlation of AHI and the standard deviations of nonapneic breath-to-breath intervals (r = 0.77; p = 0.0001; Fig 4 , top, A). In addition, the apnea index (AI; r = 0.45; p < 0.0083) as well as the AHI (r = 0.64; p = 0.0001) had significant negative correlations with the kurtosis of breath-to-breath frequency distribution (Fig 4 , bottom, B). Although both AHI and AI correlated significantly with kurtosis of breath-to-breath frequency distribution, the correlation with AHI was considerably stronger.



View larger version (21K):
[in this window]
[in a new window]
[Download PPT slide]
  		Figure 4. Relation between AHI and standard deviation (top, A) and AHI and kurtosis (bottom, B) of breath-to-breath intervals in 9 control subjects (triangles) and 34 OSA patients (circles and linear regression line).

 
Influence of Sleep Stages on Breathing in Healthy Subjects and OSA Patients
In healthy subjects, breathing became more regular with non-REM sleep and deep sleep stages, compared to wakefulness or REM sleep (Fig 5 , top, A), indicated by an increase in kurtosis. For OSA patients, this increase in kurtosis with increased sleep stages was preserved, but compared to healthy subjects, kurtosis was significantly reduced in OSA patients in each sleep stage (Fig 5 , top, A). Thus, changes in distribution of sleep stages with a small reduction of time spent in sleep stage 3 and sleep stage 4 (Fig 5 , bottom, B) could not explain the reduction of kurtosis in OSA patients. In addition, we did not observe a difference in the time subjects were asleep between the groups (Fig 5 , bottom, B).



View larger version (26K):
[in this window]
[in a new window]
[Download PPT slide]
  		Figure 5. Kurtosis (top, A) and duration (bottom, B) of different sleep stages in OSA patients (hatched circles/bars) and healthy subjects (open circles/bars) [mean ± SEM; *p < 0.05].

 
Breathing Pattern in OSA Patients With CPAP Therapy
In OSA patients, where CPAP therapy was applied, episodes of apnea and hypopnea were promptly reduced during the first night of treatment. In the tachogram of all consecutive breath-to-breath intervals, the number of apneic intervals (ie, with a duration > 10 s) dramatically decreased after the initial CPAP titration period (Fig 1 , bottom, B), and thus the AI significantly decreased (42.7 ± 12.8 vs 7.9 ± 5.8; p = 0.001).

Together with the disappearance of frequent apneas, nonapneic breathing normalized. This more regular breathing is readily visible by comparing the tachograms of breath-interval duration before (Fig 1 , top, A) and after (Fig 1 , bottom, B) CPAP therapy. CPAP treatment clearly reduced the width of the distribution around the mean duration of 3 s, resulting in a more narrow appearance of the black line (Fig 1 , bottom, B). In line with this, the standard deviations of the length of these nonapneic breaths decreased (0.83 ± 0.21), compared to the untreated state (1.13 ± 0.21; p = 0.02). The frequency distribution (Fig 2 , right, C) of breath-to-breath intervals narrowed and was no longer different from that of control subjects. This was indicated by a significant (p = 0.01) increase in kurtosis in treated vs untreated patients (Fig 3) . In addition, kurtosis of treated patients no longer differed from that of control subjects (Fig 3) .


   Discussion
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
We found greater variation in nonapneic breath-to-breath intervals in patients with OSA compared to healthy subjects. The extent of variation depended on the degree of disease. In addition to the presence of hypopnea and apnea, OSA is also characterized by a disturbance in the pattern of normal-length breaths.

Breathing Pattern in Healthy Subjects
Recurring changes in the rate of breathing was noticed 35 years ago in healthy subjects with short changes in respiratory frequency every three to four breaths superimposed on greater and more prolonged periodic changes.20 These nonperiodic short changes could not be attributed solely to random fluctuations. The average length of increase and decrease in breathing frequency, and hence the factors controlling it, were to some extent characteristic for an individual subject.21 This was later confirmed in identical twins, where pattern of breathing was significantly similar within twin pairs,22 and in another study of healthy adults, where the unique characteristics of breathing pattern were maintained over a time period of 4 to 5 years.23

Various mechanisms, which were previously reviewed,24 are thought to cause temporal variations in the pattern of breathing. Periodic variations were contributed to oscillations originated in chemoreflex feedback loops and to central neural memory mechanisms. Nonrandom, nonperiodic variability of respiratory pattern was attributed to nonlinear interactions between pulmonary and airway afferent activities and integrative central respiratory mechanisms.

Breath-by-breath variations decrease in non-REM sleep compared to wakefulness.25 26 27 This is in accordance with our finding in healthy subjects that the distribution of breath-to-breath intervals was narrow during non-REM sleep.

It has been reported that the inspiratory flow curve changes in patients with OSA from a regular sinusoidal shape to a more flattened shape during elevated upper-airway resistance.28 This would influence measurements of mere inspiratory time and could lead to a wider distribution of breath intervals in OSA patients compared to healthy subjects. We therefore chose to analyze total interval duration only. Analyzing total interval duration is also supported by the fact that expiratory duration is linearly dependent on inspiratory duration.29 However, there is no dependence of inspiratory duration on expiratory timing.30

Breathing Pattern in OSA Patients
The site of obstruction in OSA is thought to be the upper or lower pharynx, where the inward collapsing action of subatmospheric intrapharyngeal pressure during inspiration is normally prevented by an adequate tone of the genioglossus muscle.31 In patients with OSA, maximum pharyngeal area tends to be decreased for a variety of reasons.32 During wakefulness, this anatomic narrowing is compensated by increased genioglossal muscle tone. However, during sleep the compensation is lost.33

Because these features are continuously present, they will also continuously affect breathing, although the extent may vary depending on the presence of confounding factors. After inspiratory resistive loading in healthy subjects, an increase in the scatter of inspiratory time intervals has been observed.12 Magnitude and changes in scatter depend on the extent of load. Similarly, varying degrees of upper-airway narrowing in OSA patients during sleep34 may be followed by an increase in breathing variability, as has been observed in the present study.

Our finding of an increase in breathing variability in OSA patients cannot be explained by the appearance of a few breaths of extraordinary duration in comparison to a bulk of breath intervals that were unchanged, as may be suggested by a mere increase in standard deviations. Rather, this increase in breathing variability was caused by a change in the distribution of breath-to-breath intervals with normal length because kurtosis of these intervals was reduced. In addition, the increased fluctuation in the duration of each breath was not restricted to periods immediately after apnea, where a compensatory increase in breath flow is known to occur. Periods without apneic breathing also showed a reduction of kurtosis, compared to healthy subjects.

The increased breathing variability of OSA patients was present during all sleep stages. Thus, the observed reduction in stage 3 and stage 4 sleep in OSA patients, where breathing variability is decreased in healthy subjects as well as in OSA patients, was not responsible for the increase in breathing variability in OSA patients.

An age-dependent increase in breath-to-breath variability was suggested by some authors.25 26 35 Therefore, an age-dependent difference in kurtosis between the two groups had to be excluded because the 10-year difference in mean age was statistically significant. Two factors oppose the possibility that the difference in breathing variability could be explained by age. First, within the group of patients with OSA, the younger and older patients did not differ in terms of mean kurtosis, as shown in Table 2 . Secondly, the more severely affected OSA patients, who exhibited a greater breathing variability than the less affected OSA patients, tended to be even younger than the less affected patient group. The lack of an age-dependent effect in the present study might be explained by the relatively small difference in age between the two groups, compared to previous studies where an age dependence of breathing patterns was examined. In these studies, the age difference between the study groups was much greater (approximately 40 years),25 26 35 the mean age of the younger subjects was much lower (25 to 29 years), and the mean age of the older subjects was much higher (69 to 76 years).

Compared to the considerable variability in AHI for an individual patient,36 37 38 39 there appears to be a good correlation between kurtosis and AHI in the present study. This suggests that there might be a common underlying cause for the increase in breath-to-breath variability and the increase in the severity of OSA. The link might be the increase in upper-airway resistance, because a significant increase in breathing variability was also reported during snoring episodes leading to arousals in patients without OSA.40 Increases in upper-airway resistance could thus lead to increases in breath-to-breath variability, but will finally result in apnea if a threshold value is reached.

Effective CPAP therapy did not only reduce episodes of apnea and hypopnea, but also normalized breathing variability in the present study. This normalization of breathing pattern with the onset of CPAP therapy indicates that the altered breathing pattern in OSA patients is likely because of peripheral mechanisms such as the proposed increase in respiratory resistance, rather than to primarily central effects or to an altered chemoreceptor sensitivity.

Analyzing the contour of the flow signal instead of its timing provided similar evidence for a relationship between flow limitations and changes in flow signal in a study28 of CPAP titration in OSA patients. CPAP levels entirely eliminating upper-airway resistance in this study resulted in a regularly shaped inspiratory flow signal. In contrast, a high resistance was associated with a flattened inspiratory flow signal. The change to a more flattened flow contour remained detectable at suboptimal CPAP pressure that led to a decrease in apneic events with a concomitant increase in hypopneas. In this situation, esophageal pressure remained elevated.41 A similar classification of the shape of inspiratory flow curves in 10 patients with various degrees of upper-airway resistance (ie, OSA, UARS, and snoring) also indicates a relation between upper-airway resistance and changes in breathing pattern,11 as is suggested by our observations. Additional strength is added to this interpretation by a stronger correlation of kurtosis with AHI rather than AI alone in the present study.

The greater variation in breath-to-breath interval described in this study may constitute a characteristic feature of OSA. Although a direct relation between UARS and increased breath-to-breath variability remains to be shown, we have demonstrated significantly increased breath-to-breath variability in OSA patients correlating to the extent of upper-airway obstruction as indicated by AHI. This variability resolved immediately with the onset of effective CPAP therapy. Analysis of breath-to-breath intervals might therefore prove to be a valuable diagnostic tool to assess upper-airway obstruction during sleep. It seems likely that increased resistance without overt obstruction, as is characteristic for UARS, can be detected with this technique. However, this will have to await further evaluation. In the future it may be possible to incorporate breathing variability into automated control algorithm for CPAP adjustment in order to avoid not only overt complete airway obstruction by autoadjusted CPAP, but also the increase in upper-airway resistance that might be indicated by increased breathing variability.


   Footnotes
 
Abbreviations: AHI = apnea-hypopnea index; AI = apnea index; CPAP = continuous positive airway pressure; OSA = obstructive sleep apnea; REM = rapid eye movement; UARS = upper-airway resistance syndrome

Received for publication January 12, 2000. Accepted for publication September 6, 2000.


   References
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Young, T, Palta, M, Dempsey, J, et al (1993) The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 328,1230-1235[Abstract/Free Full Text]
  2. Strollo, PJ, Jr, Rogers, RM (1996) Obstructive sleep apnea. N Engl J Med 334,99-104[Free Full Text]
  3. Shepard, JWJ (1990) Cardiopulmonary consequences of obstructive sleep apnea. Mayo Clin Proc 65,1250-1259[ISI][Medline]
  4. Nasser, S, Rees, PJ (1992) Sleep apnoea: causes, consequences and treatment. Br J Clin Pract 46,39-43[ISI][Medline]
  5. He, J, Kryger, MH, Zorick, FJ, et al (1988) Mortality and apnea index in obstructive sleep apnea: experience in 385 male patients. Chest 94,9-14[Abstract/Free Full Text]
  6. Badr, MS (1999) Pathogenesis of obstructive sleep apnea. Prog Cardiovasc Dis 41,323-330[Medline]
  7. Loube, DI, Andrada, TF (1999) Comparison of respiratory polysomnographic parameters in matched cohorts of upper airway resistance and obstructive sleep apnea syndrome patients. Chest 115,1519-1524[Abstract/Free Full Text]
  8. Exar, EN, Collop, NA (1999) The upper airway resistance syndrome. Chest 115,1127-1139[Abstract/Free Full Text]
  9. Strohl, KP, Redline, S (1996) Recognition of obstructive sleep apnea. Am J Respir Crit Care Med 154,279-289[ISI][Medline]
  10. Guilleminault, C, Stoohs, R, Clerk, A, et al (1993) A cause of excessive daytime sleepiness: the upper airway resistance syndrome. Chest 104,781-787[Abstract/Free Full Text]
  11. Hosselet, J-J, Norman, RG, Ayappa, I, et al (1998) Detection of flow limitation with a nasal cannula/pressure transducer system. Am J Respir Crit Care Med 157,1461-1467[Abstract/Free Full Text]
  12. Brack, T, Jubran, A, Tobin, MJ (1998) Effect of resistive loading on variational activity of breathing. Am J Respir Crit Care Med 157,1756-1763[Abstract/Free Full Text]
  13. Brack, T, Jubran, A, Tobin, MJ (1997) Effect of elastic loading on variational activity of breathing. Am J Respir Crit Care Med 155,1341-1348[Abstract]
  14. Loube, DI, Gay, PC, Strohl, KP, et al (1999) Indications for positive airway pressure treatment of adult obstructive sleep apnea patients: a consensus statement. Chest 115,863-866[Abstract/Free Full Text]
  15. Rechtschaffen, A, Kales, A (1968) A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Brain Information Service/Brain Research Institute Los Angeles, CA.
  16. EEG arousals: scoring rules and examples; a preliminary report from the Sleep Disorders Atlas Task Force of the American Sleep Disorders Association. Sleep 1992; 15:173–184
  17. Moser, NJ, Phillips, BA, Berry, DT, et al (1994) What is hypopnea, anyway? Chest 105,426-428[Abstract/Free Full Text]
  18. Gottlieb, DJ, Whitney, CW, Bonekat, WH, et al (1999) Relation of sleepiness to respiratory disturbance index: the Sleep Heart Health Study. Am J Respir Crit Care Med 159,502-507[Abstract/Free Full Text]
  19. Press WH, Flannery BP, Teukolsky SA, et al. Statistical description of data. In: Numerical recipes in Pascal. 1st ed. Cambridge, UK: Cambridge University Press 1989; 498–546
  20. Priban, IP (1962) An analysis of some short-term patterns of breathing in man at rest. J Physiol 166,425-434
  21. Shea, SA, Walter, J, Murphy, K, et al (1987) Evidence for individuality of breathing patterns in resting healthy man. Respir Physiol 68,331-344[CrossRef][ISI][Medline]
  22. Shea, SA, Benchetrit, G, Dinh, TP, et al (1989) The breathing patterns of identical twins. Respir Physiol 75,211-223[CrossRef][ISI][Medline]
  23. Benchetrit, G, Shea, SA, Dinh, TP, et al (1989) Individuality of breathing patterns in adults assessed over time. Respir Physiol 75,199-209[CrossRef][ISI][Medline]
  24. Bruce, EN (1996) Temporal variations in the pattern of breathing. J Appl Physiol 80,1079-1087[Abstract/Free Full Text]
  25. Shore, ET, Millman, RP, Silage, DA, et al (1985) Ventilatory and arousal patterns during sleep in normal young and elderly subjects. J Appl Physiol 59,1607-1615[Abstract/Free Full Text]
  26. Tobin, MJ, Mador, MJ, Guenther, SM, et al (1988) Variability of resting respiratory drive and timing in healthy subjects. J Appl Physiol 65,309-317[Abstract/Free Full Text]
  27. Morrell, MJ, Harty, HR, Adams, L, et al (1996) Breathing during wakefulness and NREM sleep in humans without an upper airway. J Appl Physiol 81,274-281[Abstract/Free Full Text]
  28. Condos, R, Norman, RG, Krishnasamy, I, et al (1994) Flow limitation as a noninvasive assessment of residual upper-airway resistance during continuous positive airway pressure therapy of obstructive sleep apnea. Am J Respir Crit Care Med 150,475-480[Abstract]
  29. Clark, FJ, von Euler, C (1972) On the regulation of depth and rate of breathing. J Physiol 222,267-296[Abstract/Free Full Text]
  30. Rafferty, GF, Evans, J, Gardner, WN (1995) Control of expiratory time in conscious humans. J Appl Physiol 78,1910-1920[Abstract/Free Full Text]
  31. Remmers, JE, DeGroot, WJ, Sauerland, EK, et al (1978) Pathogenesis of upper airway occlusion during sleep. J Appl Physiol 44,931-938[Free Full Text]
  32. Isono, S, Remmers, JE, Tanaka, A, et al (1997) Anatomy of pharynx in patients with obstructive sleep apnea and in normal subjects. J Appl Physiol 82,1319-1326[Abstract/Free Full Text]
  33. Remmers, JE, Lahiri, S (1998) Regulating the ventilatory pump: a splendid control system prone to fail during sleep. Am J Respir Crit Care Med 157,S95-S100
  34. Schwartz, AR, O’Donnell, CP, Baron, J, et al (1998) The hypotonic upper airway in obstructive sleep apnea. Am J Respir Crit Care Med 157,1051-1057[Abstract/Free Full Text]
  35. Tobin, MJ, Chadha, TS, Jenouri, G, et al (1983) Breathing patterns: 1. Normal subjects. Chest 84,202-205[Abstract/Free Full Text]
  36. Parra, O, García-Esclasans, N, Montserrat, JM, et al (1997) Should patients with sleep apnoea/hypopnoea syndrome be diagnosed and managed on the basis of home sleep studies? Eur Respir J 10,1720-1724[Abstract]
  37. Fanfulla, F, Patruno, V, Bruschi, C, et al (1997) Obstructive sleep apnoea syndrome: is the "half-night polysomnography" an adequate method for evaluating sleep profile and respiratory events? Eur Respir J 10,1725-1729[Abstract]
  38. Wittig, RM, Romaker, A, Zorick, FJ, et al (1984) Night-to-night consistency of apneas during sleep. Am Rev Respir Dis 129,244-246[ISI][Medline]
  39. Mosko, SS, Dickel, MJ, Ashurst, J (1988) Night-to-night variability in sleep apnea and sleep-related periodic leg movements in the elderly. Sleep 11,340-348[ISI][Medline]
  40. Bloch, KE, Li, Y, Sackner, MA, et al (1997) Breathing pattern during sleep disruptive snoring. Eur Respir J 10,576-586[Abstract]
  41. Montserrat, JM, Ballester, E, Olivi, H, et al (1995) Time-course of stepwise CPAP titration: behavior of respiratory and neurologic variables. Am J Respir Crit Care Med 152,1854-1859[Abstract]



This article has been cited by other articles:


Home page
 Eur Respir JHome page
M. Miyata, N. Burioka, T. Sako, H. Suyama, Y. Fukuoka, K. Tomita, S. Higami, and E. Shimizu
A short daytime test using correlation dimension for respiratory movement in OSAHS
Eur. Respir. J., June 1, 2004; 23(6): 885 - 890.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF) Free
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Article Archive
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (3)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Kowallik, P.
Right arrow Articles by Wirtz, H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Kowallik, P.
Right arrow Articles by Wirtz, H.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS