Table of Contents  
ORIGINAL ARTICLE
Year : 2016  |  Volume : 10  |  Issue : 3  |  Page : 324-329

Effectiveness of nocturnal oximetry in predicting obstructive sleep apnea hypopnea syndrome: value of nocturnal oximetry in prediction of obstructive sleep apnea hypopnea syndrome


1 Chest Medicine Department, Faculty of Medicine, Mansoura University, Egypt
2 Chest Department, Mansoura University Hospital, Mansoura, Egypt

Date of Submission08-Feb-2016
Date of Acceptance29-Mar-2016
Date of Web Publication9-Nov-2016

Correspondence Address:
Lucy A Suliman
Samia El Gamal Street, Elmansoura 23315
Egypt
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/1687-8426.193647

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  Abstract 

Background Polysomnography (PSG) is the gold standard for diagnosing obstructive sleep apnea (OSA). However, it is time-consuming, expensive and requires technical expertise. Thus, a number of alternatives to PSG have been proposed. The present study was conducted to analyse the sensitivity, specificity and accuracy of night oximetry as a diagnostic tool in patients suspected to have sleep apnea hypopnea syndrome (SAHS), and to reduce the number of saved PSGs.
Patients and methods In total, 40 middle-aged patients clinically suspected to have Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) were included in the study. They were classified into two groups: group I (the SAHS group), comprising 33 patients with apnea hypopnea index greater than or equal to 5; and group II (the non-SAHS group), comprising seven patients with apnea hypopnea index l than 5. All patients were subjected to the following: (a) OSA screening questionnaire; (b) BMI in kg/m2, neck circumference in cm, and cardiac, chest and ENT examinations; (c) investigation in the form of arterial blood gases, chest radiograph, ECG and spirometry; and (d) full PSG and overnight oximetry, which were carried out simultaneously.
Results The baseline values of O2 saturation derived from PSG and oximetry were 93.33±2.32 and 91.50±2.79, respectively. The overnight oxygen desaturation index of oximetry was significantly lower in the SAHS group. Minimal SpO2 of PSG was significantly lower in the SAHS group. The best predicted cutoff value of overnight pulse oximetry using oxygen desaturation index for mild to moderate OSA patient diagnosis was 14.78, with 87.88% sensitivity and 88.71% specificity. However, the optimal cutoff value for severe OSA diagnosis was 52.55, with 86.67% sensitivity and 96% specificity.
Conclusion Overnight pulse oximetry may be considered a diagnostic tool in patients suspected to have SAHS, with excellent diagnostic sensitivity, specificity and accuracy, which increased with severity.

Keywords: apnea hypopnea index, night oximetry, obstructive sleep apnea, polysomnography, sleep apnea hypopnea syndrome


How to cite this article:
Suliman LA, Shalabi NM, Elmorsy SA, Moawad Mona M K. Effectiveness of nocturnal oximetry in predicting obstructive sleep apnea hypopnea syndrome: value of nocturnal oximetry in prediction of obstructive sleep apnea hypopnea syndrome. Egypt J Bronchol 2016;10:324-9

How to cite this URL:
Suliman LA, Shalabi NM, Elmorsy SA, Moawad Mona M K. Effectiveness of nocturnal oximetry in predicting obstructive sleep apnea hypopnea syndrome: value of nocturnal oximetry in prediction of obstructive sleep apnea hypopnea syndrome. Egypt J Bronchol [serial online] 2016 [cited 2019 Nov 13];10:324-9. Available from: http://www.ejbronchology.eg.net/text.asp?2016/10/3/324/193647


  Introduction Top


The obstructive sleep apnea (OSA) syndrome is a common sleep-related breathing disorder with prevalence ranging from 5 to 15% among the general population [1]. Polysomnography (PSG) is considered the gold standard for its diagnosis; it is time-consuming, expensive and needs technical expertise [2]. The waiting time to diagnose patients suspected to have OSA is long even in the developed countries; for example, it ranges from 0 to 48 months in the UK, a few weeks to more than a year in the USA and 8 to 30 months in Canada [3]. The waiting time ranges from 2 to 3 months in our sleep disordered breathing unit.

As a result, primary care providers may be reluctant to order PSG, and, also, patients often are unwilling to attend it; therefore, a number of alternatives to PSG have been proposed [4].

Oximetry has widespread availability but the results from previous studies have varied in sensitivities, ranging from 40 to 100% [5],[6],[7],[8],[9]. The diagnostic performance of an automated analysis algorithm based on falls and recovery of digitally recorded oxygen saturation was compared with PSG [10].

Automated analysis night oximetry has been evaluated in patients with sleep apnea hypopnea syndrome (SAHS) because it analyses arterial oxygen desaturation, one of the sequelae of SAHS [11].

Most countries with universal health coverage have a large number of patients who need sleep disorders clinics to be diagnosed and managed with limited resources [12].

The aim of this study was to analyse the sensitivity, specificity and accuracy of night oximetry as a diagnostic tool in patients suspected to have SAHS, and to reduce the number of PSGs that could have been saved if the diagnosis of SAHS had been established by using this method.

Patient and methods

This case study was carried out in the Sleep Disorders Breathing Unit, Chest Department of the Mansoura University Hospital, Egypt. All participants were enrolled from October 2013 to January 2014. Local ethical approval had been obtained from Mansoura University.

The study comprised 40 middle-aged patients clinically suspected to have OSAHS. They were recruited from the sleep outpatient clinic. They were classified according to apnea hypopnea index (AHI) into two groups:

Group I (the SAHS group), which comprised 33 patients with AHI greater than 5.

Group II (the non-SAHS group), which comprised seven patients with AHI less than 5.

Exclusion criteria

The exclusion criteria included age less than 18 years, patients with BMI greater than 40 kg/m2, history of neuromuscular disease or stroke, pulmonary or cardiac diseases associated with ventilatory or diffusion defect daytime hypoxaemia or hypercapnia or central sleep apnea.

All patients were subjected to the following:

  1. Full history taking with special stress on age, sex, occupation and symptoms suggestive of OSAHS (excessive daytime sleepiness, nocturnal choking, snoring and witnessed apnea); and OSA screening questionnaire such as the Stopbang, Berlin questionnaire and the Epworth Sleepiness Scale (ESS).
  2. General examination with stress on BMI in kg/m2, neck circumference (NC) in cm and cardiac, chest and ENT examinations.
  3. Routine investigations in the form of complete blood count, liver and kidney functions, arterial blood gases, chest radiograph, ECG and spirometry.
  4. All patients were admitted to the sleep laboratory and underwent full PSG and overnight oximetry, which were performed simultaneously.


Polysomnography

PSG data were recorded by a computerized PSG system (somno screen plus; SomnoMedics, GmbH, Randersacker, Germany). This included a standardized montage: two-channel electroencephalograms (C4/A1, C3/A2), bilateral electro-oculograms, submental electromyogram, bilateral leg electromyograms and ECG. Airflow was measured using a thermistor (Healthdyne Technologies, SomnoMedics, GmbH, Randersacker, Germany), respiratory effort was assessed by inductance plethysmography, and oxygen saturation was recorded using a finger probe. The oxygen saturation signal was digitally sampled at 1 Hz and stored both on the PSG record and in a separate monitor for offline analysis.

Pulse oximetry

The Nonin Wrist pulse oximetry 3100 (Nonin, Plymouth, Minnesota, USA) was used to compare its diagnostic accuracy with full-night PSG. Wrist oximetry was attached to the participant’s finger using a flexible probe. The instrument detects 20 data points per minute, each point representing the lowest saturation in a 3-s interval. A desaturation event was considered when the haemoglobin saturation level (SaO2) fell below 3% from baseline saturation. Baseline saturation was considered as the mean saturation in the previous minute. Oximetry values were periodically checked using arterial blood gas samples. The signals were digitalized and recorded using software, and were manually reviewed by two observers blinded to the polysomnographic data. The total number of desaturations was divided by the hours in bed, and an oxygen desaturation index (ODI) per hour was obtained for each patient to achieve the best cutoff point with high sensitivity and specificity.

Statistical analysis

Data were analyzed using SPSS (Statistical Package for Social Sciences; IBM Corporation Inc, USA) version 15. Qualitative data were presented as number and percentage. Comparison between groups was carried out by using the χ2-test. Quantitative data were tested for normality by using the Kolmogorov–Smirnov test. Normally distributed data were presented as mean±SD. Student’s t-test was used to compare between the two groups. Pearson’s correlation coefficient was used to test correlation between variables. The receiver operating characteristic curve analysis was also used as it has the ability to discriminate diseased cases from normal cases. A P value of less than 0.05 was considered to be statistically significant.


  Results Top


The study was conducted on 40 patients with possible OSA; 55% of them were males. The mean age was 45.55±10.37 years, BMI mean 40.21±8.91 kg/m2 and mean of NC was 32.5±8.4 cm. The mean FEV1/FVC ratio was 83.62±8.15. The baseline O2 saturation derived from PSG and oximetry were 93.33±2.32 and 91.50±2.79, respectively ([Table 1]).
Table 1: Demographic data and patient characteristics

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The BMI and NC were significantly increased in the SAHS group than in the non-SAHS group. Moreover, both ESS and Stopbang were significantly higher in the SAHS group compared with the non-SAHS group ([Table 2]).
Table 2: Anthropometric parameters and OSA screening questionnaires in the SAHS group versus the non-SAHS group

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The overnight ODI of oximetry was significantly higher in the SAHS group. Both basal SpO2 of oximetry and of PSG were significantly lower in the SAHS group compared with the non-SAHS group. Minimal SpO2 of PSG was significantly lower in the SAHS group. The SpO2 time less than 90% was significantly higher in the SAHS group. On the other hand, arousal index was significantly higher in the SAHS group compared with the non-SAHS group ([Table 3]).
Table 3: PSG and overnight pulse oximetry variables in the SAHS group versus the non-SAHS group

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The best predicted cutoff values of overnight pulse oximetry using ODI for mild to moderate OSA patient diagnosis was 14.78, with 87.88% sensitivity, 88.71% specificity, 87.50% accuracy and an area under the curve of 0.903 ([Table 4] and [Figure 1]). However, the optimal cutoff value for severe OSA diagnosis was 52.55, with 86.67% sensitivity, 96% specificity, 92.50 accuracy and an area under the curve of 0.915 ([Table 4] and [Figure 2]).
Table 4: Validity of predicted cutoff values for diagnosis of OSA depending on ODI of overnight pulse oximetry

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Figure 1: Receiver operating characteristic (ROC) curves of oxygen desaturation index (ODI) parameters for the diagnosis of mild and moderate obstructive sleep apnea (OSA) patients with thresholds apnea hypopnea index (AHI)=5–30.

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Figure 2: Receiver operating characteristic (ROC) curves of oxygen desaturation index (ODI) parameters for the diagnosis of severe obstructive sleep apnea (OSA) patients with thresholds apnea hypopnea index (AHI) > 30.

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There was a strong positive correlation between AHI and ODI of overnight pulse oximetry (r=0.819 and P=0.000) ([Figure 3]).
Figure 3: Correlation between apnea hypopnea index (AHI) and oxygen desaturation index (ODI) of overnight pulse oximetry.

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  Discussion Top


PSG is considered the gold standard for diagnosing OSA. However, it is time-consuming, technically difficult and expensive. Prediction of OSA using questionnaires, clinical features and physiological examination has been previously studied as a predictive method for diagnosing patients with OSA [13]. In addition, nocturnal pulse oximetry has been used as a diagnostic approach for OSA syndrome in the last decade [14].

Pulse oximetry has been considered as a valuable screening tool, although its effectiveness in screening patients with OSA has been debated for several years [15]. A number of studies have assessed its usefulness [16].

This study was carried out on 40 patients suspected to have OSA and no other concomitant heart or lung diseases or neurological diseases that may affect pulmonary haemodynamics.

Our study revealed that patients with SAHS were older (P<0.003) and had more BMI (P<0.029). This indicates that obesity is one of the important risk factors for OSA. This was reported by Simpson et al. (2010) [17], who demonstrated that the effect of obesity in OSA may be due to mechanical mass and biochemical mediators. Furthermore, other risk factors like male sex, older age, family history, and smoking, central obesity and larger NC were found to significantly increase the risk for OSA [18],[19].

The ESS score and that of Stopbang questionnaire were significantly higher in the SAHS group compared with the non-SAHS group (P<0.007 and 0.038, respectively), which signify that both questionnaires may be predictors of OSA. Chung et al. [20] reported that when the Stopbang score was greater than or equal to 3 (any three positive items), the sensitivity and specificity for identifying moderate–severe OSA was 87 and 31%, respectively. In addition, Stevens [21] found significant positive correlation between ESS and AHI, and concluded that if ESS was more than 10, sleep-related breathing disorders should be suspected.

Our study revealed significant lower basal SpO2 and minimum SpO2 in SAHS patients compared with non-SAHS patients, and there were significant higher SpO2 less than 90% in SAHS patients compared with non-SAHS patients (P<0.017 and P<0.000). This was in agreement with the findings of a study by Fanfulla et al. (2008) [22], who reported significant lower basal SpO2 (92±1.6 vs. 95±1.7) (P=0.005) in SAHS patients in comparison with non-SAHS patients, and also minimum SpO2 was significantly lower in SAHS patients than in non-SAHS patients (72±13 vs. 85±4, P<0.001).

This study evaluated the diagnostic performance of pulse oximetry by detecting cutoff values for the diagnosis of OSA in different stages using the receiver operating characteristic curve. The optimal cutoff values of ODI of overnight pulse oximetry for mild to moderate OSA patient diagnosis was 14.78, with 87.88% sensitivity, 88.71% specificity and 87.50% accuracy. These results were in agreement with those of Huang et al. (2015) [3] who reported that ODI parameters provided by overnight oximetry measurements may become good predictors in the diagnosis of moderate OSA with the optimal cutoff values of overnight pulse oximetry of 21.2 with 88.53% sensitivity, 85.34% specificity and 87.77% accuracy [3].

However, the optimal cutoff value of overnight pulse oximetry for severe OSA diagnosis was 52.55 with 86.67% sensitivity, 96% specificity and 92.50% accuracy. This was also demonstrated by Huang et al. (2015) [3] who noticed increased sensitivity, specificity and accuracy of overnight pulse oximetry in diagnosing severe OSA than in mild to moderate OSA.

There was a strong positive correlation between AHI and ODI of overnight pulse oximetry (P=0.000), which indicates validity of overnight pulse oximetry. This is in agreement with the findings of a study by Dumitrache-Rujinski et al. [14] who demonstrated a significant positive correlation between ODI of night oximetry and AHI (P<0.001). Moreover, they concluded that the assessment of the desaturation index by nocturnal pulse oximetry maintains its utility as a screening method for OSAS.

Orr et al. (1994) [23] studied the validity of overnight pulse oximetry as a diagnostic tool for OSA, and reported sensitivity of 100% and a specificity of 93% for diagnosing OSA (AHI>15/h). On the other hand, Chiner et al. (1999) [11] reported sensitivity of overnight pulse oximetry at different cutoff points ranging between 82% (ODI-5) and 62% (ODI-15), whereas specificity varied between 76% (ODI-5) and 93% (ODI-15). The accuracy for each ODI was 0.81, 0.75 and 0.69, respectively.

Vazquez et al. (2000) [10] using an automated analysis oximetry data and a desaturation event definition (4% lower than baseline) reported a very high sensitivity of 98% and specificity of 88%; however, this study used a definition of hypopnea without arousal, which differs from the criteria proposed by the  Atlas More Details Task Force [24]. As a result, their definition of hypopnea differs from ours. These investigators found that the addition of arousal-based scoring criteria (using their definition of arousal) for hypopnea causes only small changes in the AHI [25]. However, a large study found that incorporating arousals on the basis of the Atlas Task Force criteria on the hypopnea definition does impact on the value of the AHI [26].

Recently it was demonstrated that overnight oximetry recording appears to be a very sensitive and specific screening method for diagnosing OSAHS [27]. Pulse oximetry is accepted as the sole diagnostic evaluation criterion in the USA, Australia and Sweden [28]. On the other hand, the Apnea Task Group of the German Society for Sleep Research and Sleep Medicine stated that pulse oximetry can be employed to attain a tentative diagnosis that requires further evaluation at a sleep laboratory.

The sensitivity and specificity of pulse oximetry ranged from 31 to 98% and from 41 to 100%, respectively, according to Hornero et al. (2007) [29], Raymond et al. (2003) [30] and Vazquez et al. (2000) [10].

Epstein and Dorlac (1998) [31] concluded that the use of overnight pulse oximetry as a diagnostic tool was limited because of the high false positive results.

This study could overcome these by adjusting the settings of the oximeter at 3 s. In addition, using overnight pulse oximetry simultaneously with PSG in the same environment leads to the elimination of night to night variability of the AHI. On the other hand, the population studied by Epstein and Dorlac (1998) [31] had a low prevalence of SAHS. Moreover, our unit had a specific profile in respiratory sleep disorders and 70% of the patients were referred by pulmonary physicians depending on high clinical suspicion. Thus, this high prevalence may be respectable.

Limitation of the study

One of important limitation in oximetry as diagnostic tool in OSA in this study was the lack of data about sleep stage of studied patients; in addition, there was no differentiation between central and OSA. The artefacts that can result from movements or haemoglobin percent may affect signal quality.


  Conclusion Top


Overnight pulse oximetry maybe considered a diagnostic tool in patients suspected to have SAHS, with excellent diagnostic sensitivity, specificity and accuracy, which increased with severity.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

 
  References Top

1.
Punjabi NM. The epidemiology of adult obstructive sleep apnea. Proc Am Thorac Soc 2008;5(2):136–143.  Back to cited text no. 1
    
2.
Ross SD, Allen IE, Harrison KJ, Kvasz M, Connelly J, Sheinhait IA. Systematic review of the literature regarding the diagnosis of sleep apnea: summary, evidence report/technology assessment number 1. Rockville, MD: Agency for Health Care Policy and Research (AHCPR), Department of Health and Human Services, U. S. Public Health Service; 1999. AHCPR Publication 99-E002.  Back to cited text no. 2
    
3.
Huang SH, Teng NC, Wang KJ, Chen KH, Lee HC, Wang PC. Use of oximetry as a screening tool for obstructive sleep apnea: a case study in Taiwan. J Med Syst 2015; 39(3):29.  Back to cited text no. 3
    
4.
Magalang UJ, Dmochowski J, Veeramachaneni S, Draw A, Mador MJ, El-Solh A, Grant BJB. The predicted AHI may offer a potentially simpler alternative to polysomnography. Chest 2003; 124:1694–1701.  Back to cited text no. 4
    
5.
Williams AJ, Yu G, Santiago S, Stein M. Screening for sleep apnea using pulse oximetry and a clinical score. Chest 1991; 100(3):631–635.  Back to cited text no. 5
    
6.
Douglas NJ, Thomas S, Jan MA. Clinical value of polysomnography. Lancet 1992; 339(8789):347–350.  Back to cited text no. 6
    
7.
Cooper BG, Veale D, Griffiths CJ, Gibson GJ. Value of nocturnal oxygen saturation as a screening test for sleep apnoea. Thorax 1991; 46(8):586–588.  Back to cited text no. 7
    
8.
Gyulay S, Olson LG, Hensley MJ, King MT, Allen KM, Saunders NA. A comparison of clinical assessment and home oximetry in the diagnosis of obstructive sleep apnea. Am Rev Respir Dis 1993; 147(1):50–53.  Back to cited text no. 8
    
9.
Sériès F, Marc I, Cormier Y, La Forge J. Utility of nocturnal home oximetry for case finding in patients with suspected sleep apnea hypopnea syndrome. Ann Intern Med 1993; 119(6):449–453.  Back to cited text no. 9
    
10.
Vázquez JC, Tsai WH, Flemons WW, Masuda A, Brant R, Hajduk E et al. Automated analysis of digital oximetry in the diagnosis of obstructive sleep apnoea. Thorax 2000; 55(4):302–307.  Back to cited text no. 10
    
11.
Chiner E, Signes-Costa J, Arriero JM, Marco J, Fuentes I, Sergado A. Nocturnal oximetry for the diagnosis of the sleep apnoea hypopnoea syndrome: a method to reduce the number of polysomnographies? Thorax 1999; 54:968–971.  Back to cited text no. 11
    
12.
Chiner E, Blanquer J, Arriero JM, Marco J. Obstructive sleep apnea syndrome in the Community of Valencia: current situation, study of needs and future prospects. Arch Bronconeumol 1998; 34:177–183.  Back to cited text no. 12
    
13.
Pang KP, Terris DJ. Screening for obstructive sleep apnea: an evidence-based analysis. Am J Otolaryngol 2006; 27(2):112–118.  Back to cited text no. 13
    
14.
Dumitrache-Rujinski S, Calcaianu G, Zaharia D, Toma CL, Bogdan M. The role of overnight pulse-oximetry in recognition of obstructive sleep apnea syndrome in morbidly obese and non obese patients. Maedica (Buchar) 2013; 8(3):237–242.  Back to cited text no. 14
    
15.
Netzer N, Eliasson AH, Netzer C, Kristo DA. Overnight pulse oximetry for sleep-disordered breathing in adults: a review. Chest 2001; 120(2):625–633.  Back to cited text no. 15
    
16.
Choi S, Bennett LS, Mullins R, Davies RJ, Stradling JR. Which derivative from overnight oximetry best predicts symptomatic response to nasal continuous positive airway pressure in patients with obstructive sleep apnoea? Respir Med 2000; 94(9):895–899.  Back to cited text no. 16
    
17.
Simpson L, Mukherjee S, Cooper MN, Ward KL, Lee JD, Fedson AC et al. Sex differences in the association of regional fat distribution with the severity of obstructive sleep apnea. Sleep 2010; 33(4):467–474.  Back to cited text no. 17
    
18.
Dixon JB, Schachter LM, O’Brien PE. Predicting sleep apnea and excessive day sleepiness in the severely obese: indicators for polysomnography. Chest 2003; 123(4):1134–1141.  Back to cited text no. 18
    
19.
Khoo SM, Tan WC, Ng TP, Ho CH. Risk factors associated with habitual snoring and sleep-disordered breathing in a multi-ethnic Asian population: a population-based study. Respir Med 2004; 98(6):557–566.  Back to cited text no. 19
    
20.
Chung F, Yang Y, Brown R, Liao P. Alternative scoring models of STOP-bang questionnaire improve specificity to detect undiagnosed obstructive sleep apnea. J Clin Sleep Med. 2014; 10(9):951–958.  Back to cited text no. 20
    
21.
Stevens D (ed). Evaluation of patients with sleep disorders in Sleep Medicine Secrets. In: Sleep Medicine Secrets. Hanley and Belfus; Elsevier: Chicago, Illinois; 2004, Ch 5, pp 39–45.  Back to cited text no. 21
    
22.
Fanfulla F, Grassi M, Taurino AE, D’Artavilla Lupo N, Trentin R. The relationship of daytime hypoxemia and nocturnal hypoxia in obstructive sleep apnea syndrome. Sleep 2008; 31(2):249–255.  Back to cited text no. 22
    
23.
Orr WC, Eiken T, Pegram V, Jones R, Rundell OH. A laboratory validation study of a portable system for remote recording of sleep-related respiratory disorders. Chest 1994; 105(1):160–162.  Back to cited text no. 23
    
24.
24EEG arousals: scoring rules and examples: a preliminary report from the Sleep Disorders Atlas Task Force of the American Sleep Disorders Association. Sleep 1992; 15(2):173–184.  Back to cited text no. 24
    
25.
Tsai WH, Flemons WW, Whitelaw WA, Remmers JE. A comparison of apnea-hypopnea indices derived from different definitions of hypopnea. Am J Respir Crit Care Med 1999; 159:43–48.  Back to cited text no. 25
    
26.
Redline S, Kapur VK, Sanders MH, Quan SF, Gottlieb DJ, Rapoport DM et al. Effects of varying approaches for identifying respiratory disturbances on sleep apnea assessment. Am J Respir Crit Care Med 2000; 161:369–374.  Back to cited text no. 26
    
27.
Poupard L, Philippe C, Goldman MD, Sartène R, Mathieu M. Novel mathematical processing method of nocturnal oximetry for screening patients with suspected sleep apnoea syndrome. Sleep Breath 2012; 16:419–425.  Back to cited text no. 27
    
28.
Böhning N, Schultheiß B, Eilers S, Penzel T, Böhning W, Schmittendorf E. Comparability of pulse oximeters used in sleep medicine for the screening of OSA. Physiol Measure 2010; 31:875–888.  Back to cited text no. 28
    
29.
Hornero R, Álvarez D, Abásolo D, del Campo F, Zamarrón C. Utility of approximate entropy from overnight pulse oximetry data in the diagnosis of the obstructive sleep apnea syndrome. IEEE Trans Biomed Eng 2007; 54:107–113.  Back to cited text no. 29
    
30.
Raymond B, Cayton RM, Chappell MJ. Combined index of heart rate variability and oximetry in screening for the sleep apnoea/hypopnoea syndrome. J Sleep Res 2003; 12:53–61.  Back to cited text no. 30
    
31.
Epstein LJ, Dorlac GR. Cost-effectiveness analysis of nocturnal oximetry as a method of screening for sleep apnea-hypopnea syndrome. Chest 1998; 113:97–103.  Back to cited text no. 31
    


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