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Volume 12 No. 07
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Accepted Papers

Scientific Investigations

Predicting Obstructive Sleep Apnea with Periodic Snoring Sound Recorded at Home

Anniina Alakuijala, MD, PhD1,2; Tapani Salmi, MD, PhD1,2
1Department of Clinical Neurophysiology, HUS Medical Imaging Center, Helsinki University Hospital, Finland; 2Department of Neurological Sciences, University of Helsinki, Helsinki, Finland


Study Objectives:

The cost-effectiveness of diagnosing obstructive sleep apnea (OSA) could be improved by using a preliminary screening method among subjects with no suspicion of other sleep disorders. We aimed to evaluate the diagnostic value of periodic snoring sound recorded at home.


We included 211 subjects, aged 18–83 (130 men), who were referred to our laboratory for suspicion of OSA, and had a technically successful overnight polygraphy, measured with the Nox T3 Sleep Monitor (Nox Medical, Iceland) with a built-in microphone. We analyzed the percentage of periodic snoring during the home sleep apnea study.


Apnea-hypopnea index (AHI) ranged from 0.1 to 116 events/h and the percentage of periodic snoring from 1% to 97%. We found a strong positive correlation (r = 0.727, p < 0.001) between periodic snoring and AHI. The correlation was slightly stronger among female, younger, and obese subjects. The best threshold value of the periodic snoring for predicting an AHI > 15 events/h with as high sensitivity as possible was found to be 15%. There, sensitivity was 93.3%, specificity 35.1%, and negative predictive value 75.0%.


According to our results, it is possible to set a periodic snoring threshold (15% or more) for the subject to advance to further sleep studies. Together with medical history and prior to more expensive studies, measuring periodic snoring at home is a simple and useful method for predicting the probability of OSA, in particular among women who are often unaware of their apnea-related snoring.


Alakuijala A, Salmi T. Predicting obstructive sleep apnea with periodic snoring sound recorded at home. J Clin Sleep Med 2016;12(7):953–958.


Obstructive sleep apnea (OSA) is the most common sleep disorder causing excessive daytime sleepiness, elevated risk of cardiovascular diseases, and numerous other serious health consequences leading to reduced quality of life and increased medical costs.1 Apnea-hypopnea index (AHI) quantities the severity of OSA as the sum of apneas and hypopneas (complete and partial upper airway obstructions, respectively) per hour during sleep. OSA is defined by an AHI > 15 events/h or by combining symptoms or comorbid findings and an AHI > 5 events/h.1

The gold standard for diagnosing OSA is the polysomnography (PSG), but it is expensive and cumbersome. For most subjects without suspicion of other sleep disorders than OSA, a multi-channel home sleep apnea testing is equally reliable.2 With a growing number of suspected OSA patients, the possibility of using even fewer channels for diagnostic studies has been approached.3 One promising alternative for screening purposes would be the analysis of snoring sounds.

Although snoring as a phenomenon is well known, a satisfactory objective definition of snoring in the sleep medicine field is still lacking,4 as well as established threshold level of loudness above which snoring should be measured.5 The most accepted estimate for the prevalence of snoring ranges from 20% in adult women to 40% in adult men, increasing with age.6 As many as 50% of those who snore loudly are likely to have OSA, and a substantial majority is undiagnosed.7


Current Knowledge/Study Rationale: With the growing number of suspected sleep apnea patients, the possibility of using easier methods than polysomnography for preliminary screening has recently been raised. We aimed to evaluate the diagnostic value of periodic snoring sound recorded in the home environment.

Study Impact: This study demonstrates that measuring periodic snoring at home is a simple and useful method for predicting the probability of obstructive sleep apnea. It is possible to set a periodic snoring threshold for the subject to advance to further sleep studies.

The quality of the snoring sound is determined by many factors such as the predominant sites of upper airway narrowing, sleep stage, body position, and presence and absence of sleep-disordered breathing.6 Also when OSA is present, snoring differs depending on whether it occurs between apneas or during hypopneas.8 In addition, both the quantity and quality of snoring vary from night to night.9

During the past decade, an interest in analysis of snoring sounds to find the best predictive characteristics for OSA-associated snoring has been raised, and several quite successful algorithms have been produced.8,1018 However, many of these methods appear too complicated for the clinical ambulatory use.

Very recently, a meta-analysis of the topic was published.19 It concluded the acoustic analysis as a relatively accurate method but called for larger studies with more established criteria for snore events and OSA. The number of all subjects in the total of seven included studies was 273, and the AHI thresholds varied between studies.

Almost all studies in the literature have been performed in the sleep laboratory. The home environment is more challenging than noise-free laboratories with respect to ambient noise. There are only a few studies made in the home environment.5,20

Digital voice recording and storage have developed to store all-night sound recordings on small devices. The smartphone has been shown to record snoring sounds at home and to distinguish fairly well between snoring and other sounds, like alarms, coughs, radio, and talking.21,22

Recently, an industry-sponsored study showed a promising method in regard to OSA and snoring recorded at home.23 Subjects wore a neck-worn device with a built-in microphone and accelerometer concurrently with a multi-channel home sleep testing device during three consecutive nights. Combining position and snoring data seemed a sensitive and specific way to predict AHI, especially in non-supine position and when low sleep efficiency cases were excluded.

The cost-effectiveness of diagnosing OSA could be improved by using an easily repeated, preliminary screening method among subjects with no suspicion of other sleep disorders. We hypothesized that, unlike steady snoring, periodic snoring predicts the apnea-hypopnea index (AHI) and the severity of OSA. The aim of the present study was to evaluate the diagnostic value of periodic snoring sounds measured during home sleep apnea testing.


We included 220 consecutive subjects, aged 18–83 years (136 men), who were referred to our laboratory for suspicion of OSA. Subjects were referred to us by general practitioners (65%) and specialists (35%, mostly ENT specialists and neurologists). A referral was accepted if the subject had ≥ 2 of the following symptoms: excessive daytime sleepiness, loud snoring, pauses in breathing during sleep observed by someone else, headache in the morning, shortness of breath during sleep, cognitive impairment, or depression with a suspicion of association with sleep apnea. Most of the subjects were snorers and suffered from a variable degree of daytime somnolence. Subjects used a Nox T3 Sleep Monitor (Nox Medical, Reykjavik, Iceland) to perform whole-night cardiorespiratory polygraphy at home, and only technically successful recordings were included in the study. Our Ethics Committee approved the study (30/2014), and because the study is based only on documents completed during normally scheduled outpatient visits and diagnostic procedures, no written informed consent was required.

The Nox T3 Sleep Monitor has a built-in microphone located on the chest. The snoring sound data were downloaded to a computer and audio signals were converted to European data format using the Noxturnal software (Nox Medical, Reykjavik, Iceland). To analyze the periodic snoring, the audio signal was amplitude-integrated using epochs of 100 ms. This signal was then low-pass filtered24 to include the periodic snoring signal with cyclic appearance of sound and silent periods between 8 and 80 seconds. Remarkable periodic snoring was then defined as an episode during which the area-integrated filtered signal was higher than three times the median value of the signal. Finally, the amount and percentage of these episodes versus the total time in bed was calculated. The method has been partly described previously.25

The background information collected from each subject included gender, age, BMI, and the information about previous operations due to snoring or OSA. Respiratory parameters were scored manually according to AASM criteria.26 Parameters related to sleep-disordered breathing included AHI, oxygen desaturation index of 3% (ODI3), and flow limitation index. The flow limitation was analyzed automatically by the Noxturnal software as flattening of the inspiratory portion of the nasal pressure, and the flow limitation index as the percentage of flattened inspirations as a proportion of all inspirations during the sleep period.

Statistical analyses were performed with a computerized statistical package (IBM SPSS Statistics 22.0, Armonk, NY, USA). We used the Spearman rank correlation coefficient to evaluate the association between periodic snoring and AHI in different subgroups of subjects. All p values are two-sided, and the significance level is set at 0.05 throughout. Agreement between periodic snoring and AHI was analyzed as described by Bland and Altman.27 A receiver-operating characteristics (ROC) curve for the method in the detection of OSA was also created. Multifactorial analyses were performed with linear regression. For descriptive purposes, we report values as means, standard deviations, and range.


Of 220 study subjects, 9 had previously had an operation due to snoring or OSA. Operations included tonsillectomy and nasal or palatal radiofrequency ablation. Although the differences in parameters between all subjects and subjects without an upper airway operation were statistically insignificant, we further analyzed only those 211 subjects without any such operation.

In the study population, AHI ranged from 0.1 to 116 events/h and the percentage of periodic snoring from 1 to 97 % (Table 1). Women were older than men and their AHI was lower, but periodic snoring, flow limitation index, BMI, or ODI3 did not differ statistically significantly between genders (Table 1).

Characteristics of all study subjects.


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Table 1

Characteristics of all study subjects.

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We found a strong positive correlation (r = 0.727, p < 0.001) between the percentage of periodic snoring and AHI (Figure 1). The correlation was slightly stronger in female than male subjects (Table 2). In addition, the correlation was stronger among subjects younger than 55 years than older subjects and among obese subjects (BMI > 30) than normal-weight subjects (Table 2). Nevertheless, the correlation was statistically very significant (p < 0.001) in all subgroups in the study. Bland-Altman plot shows that periodic snoring percentage, and AHI agreed well within range of various grades of sleep apnea (Figure 2).

Scatter plot for the relationship between periodic snoring and AHI.

AHI, apnea-hypopnea index.


Figure 1

Scatter plot for the relationship between periodic snoring and AHI.

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Statistical values of the method in all subjects and in several subgroups.


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Table 2

Statistical values of the method in all subjects and in several subgroups.

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Bland-Altman plot of the two diagnostic measurements of sleep apnea severity: periodic snoring percentage and AHI.

AHI, apnea-hypopnea index; SD, standard deviation.


Figure 2

Bland-Altman plot of the two diagnostic measurements of sleep apnea severity: periodic snoring percentage and AHI.

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We then built a linear regression model in order to further explore the relationship between AHI and periodic snoring with the presence of possible confounding factors. AHI was used as an outcome and the predictors were, in the beginning, as follows: periodic snoring, flow limitation, age, gender, and BMI. Because BMI correlated with both periodic snoring and AHI, it was omitted from the model. It is noteworthy that periodic snoring did not correlate with flow limitation, gender, or age. The linear regression model detected percentage of periodic snoring (p < 0.01), flow limitation index (p < 0.01), and gender (p = 0.042) as statistically significant predictors for AHI. Correlation between flow limitation index and AHI was negative (r = −0.166, p = 0.016).

Figure 3 shows the ROC curve for periodic snoring and AHI. There, the cutoff value of AHI was chosen to be 15 events/h, which is the threshold for a clinically remarkable sleep apnea even without symptoms.1 Area under curve (AUC) value was 0.841 for all subjects, and it was slightly better for females than males (not shown).

ROC curve for periodic snoring and AHI > 15 events/h.

AHI, apnea-hypopnea index; ROC; receiver-operating characteristics curve; AUC, area under curve.


Figure 3

ROC curve for periodic snoring and AHI > 15 events/h.

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The best threshold value of the periodic snoring for predicting that a subject has an AHI > 15 events/h was found to be 35%. There, the sensitivity and the specificity were 75% and 84%, respectively. The diagnostic odds ratio (DOR) was 16.6. This threshold of 35% would serve well as the only diagnostic test for OSA. Still, 25% of the subjects would be misclassified as not having the disease. Thus, we further looked at the other periodic snoring thresholds in order to increase the sensitivity which is the most important value for screening purposes. The periodic snoring threshold with as high sensitivity as possible would be 15%. There, the sensitivity, the specificity, and negative predictive value were 93%, 35%, and 75%, respectively (Table 2). All these diagnostic values of the test were slightly better among female, younger (< 55 years) and obese (BMI > 30) subjects (Table 2). If a subject was female, younger than 55 years of age, and obese, the sensitivity was 100%. Even in the opposite case of an older, non-obese male subject, the sensitivity was as acceptable as 83%. All the diagnostic values through different thresholds of periodic snoring ranging from 5% to 50% are shown in Table S1 in the supplemental material.


The suspicion of OSA is often based on hearing periodic snoring by a spouse. The logical next step would be to look at the phenomenon more closely. To the best of our knowledge, this is the largest study aiming to predict the severity of OSA with snoring sound in the home environment. The sample size was large enough to analyze differences in subgroups based on gender, age, and BMI, as well.

Periodic snoring correlated very well with the severity of OSA. Intriguingly, the correlation was stronger and the diagnostic values of the method were better for women, for younger subjects, and for obese subjects. In some studies, women with OSA were shown to complain less about snoring, but they might be unaware of it or sometimes even unwilling to report it as a socially unacceptable symptom.28 In our material, measured periodic snoring was equally common among men and women. The criteria to sleep studies was clinical suspicion of sleep apnea and the number of females referred to our laboratory was clearly lower than males, perhaps partly due to a still common stereotype of OSA as a disease of men. Female subjects were thus selected for the referral with relatively strict criteria and strong suspicion of OSA, which perhaps contributed to stronger association between periodic snoring and AHI.

The prevalence of snoring on the whole increases with age at least until the age of 70.29 In our study, older age did not increase periodic snoring. The weakening of tone of upper airway muscles during sleep may contribute to the increase of steady snoring, but perhaps not particularly periodic snoring. Thus, the diagnostic values of our present method deteriorated with subjects older than 55 years old. With higher BMI, both periodic snoring and AHI increased, and diagnostic values of the method improved with BMI over 30.

Steady snoring is associated with flow limitation when pharyngeal muscle tone is lowered during sleep, further reducing the cross-sectional area of the pharynx and leading to increased upper airway resistance.30 The inspiratory efforts during obstructive breathing contribute to excessive daytime sleepiness and other symptoms despite the lack of apneas.31 In this study of patients with suspected sleep-disordered breathing, the subjects seemed to have either a phenotype of complete airway obstruction and a lot of respiratory events or another phenotype of partial upper airway obstruction and increased inspiratory effort. As a consequence, the flow limitation index was negatively correlated to AHI. Periodic snoring did not associate with flow limitation.

According to our results, it is possible to set a periodic snoring threshold for the subject to advance to further studies. With the periodic snoring percentage of 15% or more, a subject should continue to the overnight polygraphy or PSG. This is especially true for women who have somewhat lower pre-test probability of OSA than men.

Our results are consistent with a recently published meta-analysis.19 Compared to their pooled estimates of diagnostic values, it is possible to achieve clearly higher sensitivity with our method if we put an emphasis on sensitivity while choosing the periodic snoring threshold, but to the detriment of lower sensitivity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and DOR. It is noteworthy that those other studies were made in the sleep laboratory without obtrusive noise from bed partner or home environment.

The severity of sleep apnea has been shown to vary from night to night due to proportion of supine position, proportion of rapid eye movement (REM) sleep, use of alcohol, and other contributing factors.32 An ideal approach to this uncertainty would be to obtain repeated measures, but this is often not feasible due to cost and inconvenience. A simpler method like periodic snoring sound analysis could be very beneficial to this demand.

The algorithm presented here was previously used when snoring was recorded with a Moving Picture Experts Group Layer-3 Audio (MP3) recorder.25 MP3 recorders and smart-phones are inexpensive, widely available, easily utilized with minimal instructions and without side effects for the subjects, and are thus ideal for use in screening.33

Measuring periodic snoring at home would add more to the anamnestic information prior to conventional sleep studies. This test could be checked by the referring physician especially if the patient sleeps alone and there are no available data about snoring or pauses in breathing. Even married women are often unaware of their snoring because sounds of a bed partner may not awaken male as easily as female spouses. Often women with moderate daytime sleepiness but not known to be snorers have difficulties in getting access to sleep studies, and it is delightful to notice that the method presented here seems to be fairly accurate among women in particular. In addition to the use by the physicians, measuring periodic snoring could be used as a publicly available self-assessment tool even before contacting the general practitioner.

In addition to screening purposes, the method presented in this paper could also be helpful for the follow-up of snoring after therapeutic interventions like a mandibular advancement device or non-medical interventions like weight loss or position therapy. Corresponding to blood glucose or blood pressure monitoring, a new way of OSA monitoring is attainable, as our method works accurately at home. Periodic snoring sounds can be recorded at home with a smartphone or other widely accessible devices, and analyzed at hospital or, in the future, with an integrated application.

Our study has certain limitations. Because the study was a part of a routine clinical diagnostic work in our laboratory, the ambulatory overnight polygraphy was repeated only if it was technically unsuccessful or if the subject could not sleep long enough. Thus, we could not get snoring data from more than one night. It would have been intriguing to see if the diagnostic values of the periodic snoring test had risen even higher with repeated nights. Of note, if the periodic snoring recording is performed without sleeping position data, the wrong negative result is possible in position-dependent OSA. This could be avoided by using repeated measures to see the consistency of abnormalities. Furthermore, at this point we could not directly compare the diagnostic efficiency of home sleep apnea study with and without prior screening for periodic snoring.


Measuring periodic snoring at home is a simple and useful method for predicting the probability of OSA before more expensive sleep studies.


This was not an industry supported study. Tapani Salmi has worked as a medical consultant for SVS software company from 2008 to 2011. Anniina Alakuijala declares that she has no competing interests. Both authors declare that they engaged in no off-label or investigational use of any medical products.



apnea-hypopnea index


area under curve


body mass index


diagnostic odds ratio


Moving Picture Experts Group Layer-3 Audio


negative likelihood ratio


negative predictive value


oxygen desaturation index of three percentage units


obstructive sleep apnea


positive likelihood ratio




rapid eye movement


receiver-operating characteristics curve



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